Systems and methods for managing a renewable power asset

ABSTRACT

Systems, methods, and devices may enable management of a renewable power asset. A control device may generate a Day-Ahead (DA) pricing model, a Real-Time (RT) pricing model and a renewable generation model for the renewable power asset. Optimal DA commitments may be determined, and an optimal RT schedule estimated. A DA power delivery strategy and an RT power delivery strategy may be determined. The determined DA and RT power delivery strategies may be evaluated based on obtained real power prices. The DA and RT power delivery strategies may be redetermined, and the renewable power asset may be controlled to deliver power the DA and RT power delivery strategies. The value of the renewable power asset may be maximized while bounding financial risks and returns associated with scheduling the renewable power asset as tailored to risk preferences of the renewable power asset owner or operator.

CROSS-REFERENCE TO RELATED APPLICATIONS

This utility patent application is a continuation of U.S. patentapplication Ser. No. 16/892,942, filed Jun. 4, 2020, titled “Systems AndMethods For Managing A Renewable Power Asset”, and naming inventorsBenjamin Michael Jenkins, Aly Eldeen O. Eltayeb, and Marco Ferrara,which claims priority from U.S. provisional patent application Ser. No.62/857,437, filed Jun. 5, 2019, titled “Managing A Renewable PowerAsset”, and naming inventors Benjamin Michael JENKINS, Aly Eldeen O.ELTAYEB, and Marco FERRARA.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever. Copyright 2022, Form Energy, Inc.

BACKGROUND Field of Technology

This relates to renewable power assets, and more specifically tomanaging power production and storage based on energy markets.

Background

Renewable power sources are becoming more prevalent and cost effective.However, many renewable power sources face an intermittency problem thatis hindering renewable power source adoption. The impact of theintermittent tendencies of renewable power sources may be mitigated bypairing renewable power sources with bulk energy storage systems, suchas short duration energy storage and long duration energy storage(LODES) systems.

In a price environment with Day-Ahead and Real-Time prices, the optimaldispatch of a renewable asset (for example, a solar or wind powergeneration facility, which may also include an energy storage asset)suffers from the forecast uncertainty of production volumes of the assetand the forecast uncertainty of market prices, where prices may in partbe related to production volumes. In a regulated environment withrequirements to deliver reliable power at the lowest cost, the optimalscheduling of a renewable asset (for example, a solar or wind powergeneration facility, which may also include an energy storage asset)suffers from the forecast uncertainty of production volumes of the assetand the forecast uncertainty of overall electricity demand andavailability and cost of supplying electricity from other generationassets.

A Day-Ahead (DA) energy market enables market participants to commit tobuy or sell wholesale electricity one day before the operating day, tohelp avoid price volatility. A Real-Time (RT) energy market enablesmarket participants to buy and sell wholesale electricity during thecourse of an operating day. The RT energy market may balance differencesbetween DA commitments and the actual RT demand for and production ofelectricity. The RT energy market may produce a separate, secondfinancial settlement. In some implementations, the second financialsettlement may establish a real-time locational marginal price (LMP)that may be paid or charged to participants in the DA energy market fordemand or generation that deviates from the day-ahead commitments.

A need exists to support the adoption of combined power generation,transmission, and storage systems (e.g., a power plant having arenewable power generation source paired with a bulk energy storagesystem and co-optimized transmission facilities at any of the powerplant and the bulk energy storage system).

DESCRIPTION OF PRIOR ART

U.S. Pat. No. 10,266,034 (issued 2019 Apr. 30, naming inventorMarhoefer, titled “Virtual power plant system and method incorporatingrenewal energy, storage and scalable value-based optimization”)discloses, in the Abstract, “Methods and systems provided for creating ascalable building block for a virtual power plant, where individualbuildings can incorporate on-site renewable energy assets and energystorage and optimize the acquisition, storage and consumption of energyin accordance with a value hierarchy. Each building block can beaggregated into a virtual power plant, in which centralized control ofload shifting in selected buildings, based on predictive factors orprice signals, can provide bulk power for ancillary services or peakdemand situations. Aggregation can occur at multiple levels, includingdevelopments consisting of both individual and common renewable energyand storage assets. The methods used to optimize the system can also beapplied to ‘right size’ the amount of renewable energy and storagecapacity at each site to maximize return on the capital investment.”

United States Patent Application Publication 2017/0228834 (published2017 Aug. 10, naming inventor Hoff, titled “Generating A Risk-AdjustedProbabilistic Forecast Of Renewable Power Production For A Fleet WithThe Aid Of A Digital Computer”) discloses, in the Abstract,“Probabilistic forecasts of the expected power production of renewablepower sources, such as solar and wind, are generally provided with adegree of uncertainty. The expected power production for a fleet can beprojected as a time series of power production estimates over a timeperiod ahead of the current time. The uncertainty of each powerproduction estimate can be combined with the costs and risks associatedwith power generation forecasting errors, and displayed or visuallygraphed as a single, deterministic result to assist power grid operators(or planners) in deciding whether to rely on the renewable powersource.”

“Twenty years of linear programming based portfolio optimization” fromauthors Renata Mansini, Wlodzimierz Ogryczak, and M. Grazia Speranza,published Apr. 16, 2014 in European Journal of Operational ResearchVolume 234, Issue 2, pages 518-535 discloses, in the Abstract,“Markowitz formulated the portfolio optimization problem through twocriteria: the expected return and the risk, as a measure of thevariability of the return. The classical Markowitz model uses thevariance as the risk measure and is a quadratic programming problem.Many attempts have been made to linearize the portfolio optimizationproblem. Several different risk measures have been proposed which arecomputationally attractive as (for discrete random variables) they giverise to linear programming (LP) problems. About twenty years ago, themean absolute deviation (MAD) model drew a lot of attention resulting inmuch research and speeding up development of other LP models. Further,the LP models based on the conditional value at risk (CVaR) have a greatimpact on new developments in portfolio optimization during the firstdecade of the 21st century. The LP solvability may become relevant forreal-life decisions when portfolios have to meet side constraints andtake into account transaction costs or when large size instances have tobe solved. In this paper we review the variety of LP solvable portfoliooptimization models presented in the literature, the real features thathave been modeled and the solution approaches to the resulting models,in most of the cases mixed integer linear programming (MILP) models. Wealso discuss the impact of the inclusion of the real features.”

None of the above provides 1) systems to support the optimized designand operation of renewable power generation and transmission, 2) with orwithout energy storage assets, 3) based on maximizing energy generationand dispatch value, or minimizing energy generation, storage, anddispatch costs, 4) within a rigorous framework that precisely accountsfor investor or operator risk and return preferences, 5) through linearprogramming computable optimizations. What is needed, therefore, is asolution that overcomes the above-mentioned limitations and thatincludes the features enumerated above.

BRIEF SUMMARY

Systems, methods, and devices may enable management of a renewable powerasset (for example, a renewable power generation asset, such as a solaror wind power generation facility, which may optionally also include anenergy storage facility). A processor of a control device for arenewable power asset may generate one or more of a Day-Ahead (DA)pricing model for a renewable power asset and a Real-Time (RT) pricingmodel for the renewable power asset. The processor may determine optimalDA commitments for the renewable power asset, and the processor mayestimate an optimal RT schedule for the renewable power asset. Theprocessor may determine a DA power delivery strategy and an RT powerdelivery strategy for the renewable power asset. The processor mayobtain real power prices and may evaluate the determined DA powerdelivery strategy and the determined RT power delivery strategy based onthe obtained real power prices. The processor may redetermine one ormore of the DA power delivery strategy and the RT power deliverystrategy, and the processor may control the renewable power asset todeliver power from renewable power asset based on one or more of DA andRT power delivery strategies. In a price environment with DA and RTprices, the optimal dispatch of a renewable power asset suffers from theforecast uncertainty of prices, which may in part be driven byuncertainty in production volumes of the renewable power generationasset. The processor may maximize the value of the renewable power assetand bound financial risks associated with scheduling the renewable powerasset. The processor may further perform such maximization whilebalancing between risks and returns tailored to risk preferences of therenewable power asset owner or operator.

The processor may determine a prediction of DA power generation supplyand demand that may include the renewable power asset. The processor maydetermine optimal DA commitments for the renewable power asset and mayestimate an optimal RT schedule for the renewable power asset. Theprocessor may determine a DA power delivery schedule for the renewablepower asset and an

RT power delivery schedule for the renewable power asset. The processormay obtain real power prices and may evaluate the determined DA powerdelivery strategy and the determined RT power delivery strategy based onthe obtained real power prices. The processor may redetermine one ormore of the DA power delivery schedule and the RT delivery schedule. Theprocessor may control the renewable power asset to deliver power fromrenewable power asset based on one or more of DA and RT power deliverystrategy.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, closely related figures and items have the same numberbut different alphabetic suffixes. Processes, states, statuses, anddatabases are named for their respective functions.

FIG. 1A is a system block diagram of a combined power generation,transmission and storage system.

FIG. 1B is a system block diagram of an alternate combined powergeneration, transmission and storage system.

FIG. 1C is a system block diagram of an alternate power generation andtransmission system.

FIG. 2 is a system block diagram of computing servers and communicationsin a preferred embodiment.

FIG. 3 is a process flow diagram illustrating an embodiment method formanaging a renewable power asset according to various embodiments.

DETAILED DESCRIPTION, INCLUDING THE PREFERRED EMBODIMENT

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which are shown,by way of illustration, specific embodiments which may be practiced. Itis to be understood that other embodiments may be used, and structuralchanges may be made without departing from the scope of the presentdisclosure.

Terminology

The terminology and definitions of the prior art are not necessarilyconsistent with the terminology and definitions of the currentinvention. Where there is a conflict, the following definitions apply.

A “computing device” refers to any one or all of cellular telephones,smart phones, personal or mobile multi-media players, personal dataassistants (PDAs), laptop computers, personal computers, tabletcomputers, smart books, palm-top computers, wireless electronic mailreceivers, multimedia Internet enabled cellular telephones, controllers,and similar electronic devices which include a programmable processor,memory, and circuitry configured to perform operations as describedherein.

A “server” refers to any computing device capable of functioning as aserver, such as a master exchange server, web server, mail server,document server, content server, or any other type of server. A servermay be a dedicated computing device or a computing device including aserver module (e.g., running an application that may cause the computingdevice to operate as a server). A server module (e.g., serverapplication) may be a full function server module, or a light orsecondary server module (e.g., light or secondary server application)that is configured to provide synchronization services among the dynamicdatabases on receiver devices. A light server or secondary server may bea slimmed-down version of server-type functionality that can beimplemented on a receiver device thereby enabling it to function as anInternet server (e.g., an enterprise e-mail server) only to the extentnecessary to provide the functionality described herein.

A processor refers to a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof, within a computing device. A general-purposeprocessor may be a microprocessor, but, in the alternative, may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration.

A computer network refers to a 3G network, 4G network, 5G network, localarea network, wide area network, core network, the Internet, or anycombinations thereof.

A “renewable power asset” includes one or more renewable powergeneration assets and zero or more energy storage assets. A “renewablepower asset” may also be referred to as a “node”.

A “power generation system” includes one or more power generationsources and zero or more energy storage assets. Each power generationsource may be a renewable power generation asset or a traditionalnon-renewable generation asset such as gas, coal, or nuclear. A powergeneration system may also be referred to as a “node”.

A renewable power generation asset is a power generator using arenewable resource, such as, but not limited to, wind, solar, hydro,biomass, ocean thermal, and geothermal generators.

An Energy Storage asset is equivalent to a bulk energy storage system.

A bulk energy storage system is a short duration energy storage, or longduration energy storage (LODES), and may include one or more batteries.

A Day Ahead Energy Market lets participants (energy generators and loadserving entities) commit to buy or sell electricity one day before theoperating day. The price committed is the Day Ahead (DA) price.

A Real Time Energy Market lets participants (energy generators and loadserving entities) buy and sell electricity during the course of anoperating day, and balances differences between Day Ahead commitmentsand actual real-time demand. The price exchanged within the Real TimeEnergy Market is the Real Time (RT) price.

In a regulated environment, also referred to as a regulated energymarket, one entity owns and operates production, infrastructure, anddelivery of electricity. Electricity pricing rates are set by publiccommissions and not a competitive market.

An Operating Strategy, or just “strategy”, is a mathematical/algorithmicframework for operating a renewable power asset.

An Operating Schedule, or just “schedule”, is an actual power outputschedule for a renewable power asset; i.e., when to ramp-up, generate,and ramp-down for a generator; when to charge and when to output for astorage asset; when to transmit for a transmission asset.

Renewable curtailment is reduction of output of a renewable generationresource from maximum output, and may be controlled based on anoperating schedule.

“Prediction” and “forecast” are used interchangeably herein.

The following variables and operations are defined as used throughoutthe description. Bold notation indicates vector variables that span anoptimization time horizon.

τ is defined to be a transpose operation.

⋅ is defined to be a scalar product operation.

J is defined to be a number of discretized time steps in optimizationhorizon, indexed by 1≤j≤J.

N is defined to be the number of possible DA price scenarios, indexed by1≤n≤N.

M is defined to be the number of possible renewable generationscenarios, indexed by 1≤m≤M.

K is defined to be the number of possible RT price scenarios, indexed by1≤k≤K.

I is defined to be the number of possible power demand scenarios,indexed by 1≤i≤I.

T is defined to be the number of total scenario permutations in givenoptimization, indexed by 1≤t≤T.

q_(DA) is defined to be the DA commitments.

q_(X) is defined to be the RT curtailment of renewable generation.

p_(DA) is defined to be a realized DA price.

p_(DA,n) is defined to be the DA price forecast in the n^(th) scenario.

q_(RT) is defined to be the RT renewable generation.

q_(RT,m) is defined to be the renewable generation forecast in them^(th) scenario.

p_(RT) is defined to be a RT price.

p_(RT,k) is defined to be the RT price forecast in the k^(th) scenario.

(q_(RT,m)−q_(DA)−q_(X)) is defined to be the deviation from DAcommitments.

δ_(RT,n,m,k) is defined to be the RT penalties, which is a function ofdeviation from DA commitments and market prices. For example, penaltiesmay be a multiplier of the absolute value of the deviation from DAcommitments.

w_(n,m,k) is defined to be the joint probability of (p_(DA)=p_(DA,n);q_(RT)=q_(RT,m); p_(RT)=p_(RT,k)).

γ_(m) is defined to be a factor to bias optimization towards specificgeneration forecast scenarios.

λ is defined to be a risk tolerance factor (with 0 being no riskaversion).

is defined to be the Joint probability of (q_(RT)=q_(RT,m);p_(RT)=p_(RT,k)) given realized p_(DA).

d_(RT) is defined to be the RT optimal discharge schedule of a storagesystem.

c_(RT) is defined to be the RT optimal charge schedule of a storagesystem.

SOC is defined to be the State of charge of storage system.

(q_(RT,m)+d_(RT)−c_(RT)−q_(DA)−q_(X)) is defined to be the deviationfrom DA commitments, including energy storage.

l_(RT,i) is defined to be the demand forecast in the i^(th) scenario.

(l_(RT,i)−q_(RT,m)−d_(RT)+c_(RT)+q_(X)) is defined to be Residualdemand.

c_(RT,i,m) is defined to be the cost of residual demand, which is afunction of residual demand representing the short term running cost ofa marginal generator called upon fulfilling the last unit of residualdemand.

w_(i,m) is defined to be the joint probability of (l_(RT)=l_(RT,i);q_(RT)=q_(RT,m)).

P_(ESS) is defined to be storage rated power.

c_(P) is defined to be storage unit power cost amortized in theoptimization horizon.

E_(ESS) is define to be storage rated energy.

c_(E) is defined to be storage unit energy cost amortized in theoptimization horizon.

Operation

Systems, methods, and devices may enable the efficient scheduling of arenewable power asset. Methods may be implemented in software orhardware and executed by a processor of a computing device directing oroperating with one or more control devices to enable a renewable powerasset to optimize operation of the renewable power asset, includingdetermination of bounds and the active management of financial risksassociated with scheduling and operation of the renewable power asset.

The processor may generate forecasts of Day-Ahead (DA) and Real-Time(RT) prices of power that may be provided from a renewable power asset.The processor may generate the forecasts using, for example, predictiontechniques such as regression that includes information from a varietyof sources including one or more of historical weather and weatherforecasts, historical power supply, historical power demand, historicalnodal transmission characteristics, and well as indicators of marketsentiment. For example, forecasts of expected demand may be used asindicators of market sentiment by high or low demand correlating withhigh or low energy prices. The processor may obtain data pertaining to anode under consideration including historical DA and RT prices, totalquantities committed at DA planning and total local load, historicalrenewable generation and day-ahead estimated renewable generation withassociated exceedance probabilities, historical temperature, plantoutages, and/or other similar factors. The processor may determineadditional information such as on/off peak, weekday/weekend binarysignals, and season or month categorical signals. The processor mayaugment the historical DA and RT pricing information using a fundamentalmodel that may be based at least in part on a supply stack in the regionof the node under consideration, which may provide additional fidelity.Examples of such fundamental models are linear and mixed-integer linearprograms routinely used in production cost models.

The processor may use all or a subset of the data to generate amathematical model (such as an autoregressive-moving-average model withexogenous inputs or “ARMAX”, a feedforward neural network, a recurringneural network, or similar advanced regression models known to thoseskilled in the art) which predicts the DA and/or RT pricing for multipleprediction horizons. By employing a mathematical model, the processormay extrapolate into the future historical trends of sensitivity todifferent variables to enable the generation of multiple futurescenarios with corresponding probabilities. The processor may employ oneor more statistical descriptions of historical behavior not captured inthe mathematical model, also known as residuals, to add stochasticinformation back on the model output, to accurately simulate data withproperties which resemble a true signal.

The processor may use the forecasts of DA and RT prices to identifyoptimal DA commitments and expected optimal power curtailment of therenewable power asset and optionally also the expected optimal dispatchof an energy storage asset. The processor may apply the determined DAcommitments (which may be held as fixed), may determine actual DAprices, and may use RT forecasts (e.g., with a rolling horizon) toestimate an optimal RT schedule of the renewable power asset and of anenergy storage asset, including power curtailment, across a range ofscenarios that may be weighted by their probability.

The processor may determine an optimal DA strategy and/or an optimal RTstrategy (e.g., by leveraging a mathematical method, such asmathematical programming) that may reflect, for example, preferences ofan asset owner of risk-adjusted return. The determined RT strategy mayinclude scheduling a renewable RT delivery and incurring penalties attimes when RT schedule may be short of DA commitments. The determined RTstrategy also may include buying power from RT markets to fill DAcommitments at times when an RT schedule is short of the DA commitments.

Within a regulated environment, the processor may use information, asdetailed above in generation of DA and RT price forecasts, to generateforecasts of energy supply, demand, renewable generation, and expectedcost of energy generation. Using these forecasts, the processor mayidentify one or more optimal RT operating strategy of the renewablepower asset, energy storage asset, and other power generation assets tominimize overall expected generation and storage cost while meeting theanticipated power demand.

Referring to FIG. 1A, in the preferred embodiment power generationsystem 101 may include a combined power generation, transmission, andstorage system, such as a power plant including one or more powergeneration sources 102 and one or more bulk energy storage systems 104.Power generation sources 102 may be preferably renewable powergeneration sources, but may also be non-renewable power generationssources or combinations of renewable and non-renewable power generationsources. Some examples include wind generators, solar generators,geothermal generators, and nuclear generators. Bulk energy storagesystems 104 may be short duration energy storage, LODES systems, or acombination of both, and may include one or more batteries. Someexamples include rechargeable secondary batteries, refuellable primarybatteries, and combinations of primary and secondary batteries. Batterychemistries may be any suitable chemistry, such as, but not limited to,lithium-ion based chemistries LFP, NMC, NMA, NCO, and/or Al, AlCl₃, Fe,FeO_(x)(OH)_(y), Na_(x)S_(y), SiO_(x)(OH)_(y), AlO_(x)(OH)_(y).Operation of power generation sources 102 may be controlled by one ormore control systems 106. Control systems 106 may include motors, pumps,fans, switches, relays, or any other type devices that may serve tocontrol the generation of electricity by power generation sources 102.Operation of bulk energy storage systems 104 may be controlled by one ormore control systems 108. Control systems 108 may include motors, pumps,fans, switches, relays, or any other type devices that may serve tocontrol the discharge and/or storage of electricity by the bulk energystorage systems. Control systems 106 and 108 may both be connected toplant controller 112. Plant controller 112 may monitor the overalloperation of power generation system 101 and generate and send controlsignals to control systems 106 and 108 to control the operations ofpower generation sources 102 and bulk energy storage systems 104.

In power generation system 101, power generation sources 102 and bulkenergy storage systems 104 may both be connected to one or more powercontrol devices 110. Power control devices 110 may be connected to powergrid 115 or other transmission infrastructure. Power control devices 110may include switches, converters, inverters, relays, power electronics,and any other type devices that may serve to control the flow ofelectricity from, to, or between one or more of power generation sources102, bulk energy storage systems 104, and power grid 115. Additionally,power generation system 101 may include transmission facilities 130connecting power generation system 101 to power grid 115. As an example,transmission facilities 130 may connect between power control devices110 and power grid 115 to enable electricity to flow between powergeneration system 101 and power grid 115. Transmission facilities 130may include transmission lines, switches, relays, transformers, and anyother type devices that may serve to support the flow of electricitybetween power generation system 101 and power grid 115. Power controldevices 110 and/or transmission facilities 130 may be connected to plantcontroller 112. Plant controller 112 may be a computing device which maymonitor and control the operations of power control devices 110 and/ortransmission facilities 130, such as via various control signals. Plantcontroller 112 may control power control devices 110 and/or transmissionfacilities 130 to provide electricity from power generation sources 102to power grid 115 and/or to bulk energy storage systems 104, to provideelectricity from bulk energy storage systems 104 to power grid 115,and/or to provide electricity from power grid 115 to bulk energy storagesystems 104. Power generation source 102 may selectively charge bulkenergy storage system 104 and bulk energy storage system 104 mayselectively discharge to the power grid 115. In this manner, energy(e.g., renewable energy, non-renewable energy, etc.) generated by powergeneration source 102 may be output to power grid 115 sometime aftergeneration through bulk energy storage system 104.

Power generation sources 102 and the bulk energy storage systems 104 maybe located together or geographically separated from one another. Forexample, bulk energy storage system 104 may be upstream of atransmission constraint, such as co-located with power generation source102, upstream of a portion of grid 115. In this manner, over build ofunderutilized transmission infrastructure may be avoided by situatingbulk energy storage system 104 upstream of a transmission constraint,charging bulk energy storage system 104 at times of transmissionshortage and discharging bulk energy storage system 104 at times ofavailable capacity. Bulk energy storage system 104 may also arbitrateelectricity according to prevailing market prices to increase therevenues to power generation source 102. In another example, bulk energystorage system 104 may be downstream of a transmission constraint, suchas downstream of a portion of grid 115, from power generation source102. In this manner, over build of underutilized transmissioninfrastructure may be avoided by situating bulk energy storage system104 downstream of a transmission constraint, charging bulk energystorage system 104 at times of available capacity and discharging bulkenergy storage system 104 at times of transmission shortage. Bulk energystorage system 104 may also arbitrate electricity according toprevailing market prices to reduce the final cost of electricity toconsumers.

Referring also to FIG. 1B, in an alternate embodiment power generationsources 102 and bulk energy storage systems 104 may be separated fromone another. Power generation sources 102 and bulk energy storagesystems 104 may be separated in different plants 131A and 131B,respectively. Plants 131A and 131B may be co-located or may begeographically separated from one another. Plants 131A and 131B mayconnect to power grid 115 at different places. For example, plant 131Amay connect to grid 115 upstream of where plant 131B connects. Plant131A may include its own respective plant controller 112A, power controldevices 110A, and/or transmission facilities 130A. Power control devices110A and/or transmission facilities 130A may be connected to plantcontroller 112A. Plant controller 112A may monitor and controloperations of power control devices 110A and/or transmission facilities130A, such as via various control signals. Plant controller 112A maycontrol power control devices 110A and/or transmission facilities 130Ato provide electricity from power generation sources 102 to power grid115. Plant 131B associated with bulk energy storage systems 104 mayinclude its own respective plant controller 112B, power control devices110B, and/or transmission facilities 130B. Power control devices 110Band/or transmission facilities 130B may be connected to plant controller112B. Plant controller 112B may monitor and control the operations ofpower control devices 110B and/or transmission facilities 130B, such asvia various control signals. Plant controller 112B may control the powercontrol devices 110B and/or transmission facilities 130B to provideelectricity from bulk energy storage systems 104 to power grid 115and/or from grid 115 to bulk energy storage systems 104. Respectiveplant controllers 112A, 112B and respective transmission facilities130A, 130B may be similar to plant controller 112 and transmissionfacilities 130 described with reference to FIG. 1A.

Referring also to FIG. 1C, in an alternate embodiment power generationsource 102 may be located in plant 132 that does not include a bulkenergy storage system. Power generation system 101 and plant managementsystem 121 may operate as described above with reference to FIG. 1A formonitoring and control of power generation and transmission from plant132.

Plant controller 112, or plant controllers 112A and 112B, may be incommunication with computer network 120. Using connections to network120, plant controller 112 may exchange data with network 120 as well asdevices connected to network 120, such as plant management system 121 orany other device connected to network 120. Plant management system 121may include one or more computing devices, such as computing device 124and server 122. Computing device 124 and server 122 may be connected toone another directly and/or via connections to network 120. Thefunctionality of computing device 124 and server 122 may be combinedinto a single computing device, or may split among more than twodevices. Additionally, the functionality may be in whole, or in part,offloaded to a remote computing device, such as a cloud-based computingsystem. While illustrated as in communication with a single powergeneration system 101, plant management system 121 may be incommunication with multiple power generation systems.

Referring also to FIG. 2 , computing device 124 of plant managementsystem 121 may provide user interface 200 enabling a user of plantmanagement system 121 to define inputs 204 to plant management system121 and/or power generation system 101, receive indications 208associated with plant management system 121 and/or power generationsystem 101, and otherwise control the operation of plant managementsystem 121 and/or power generation system 101. A user may utilizecomputing device 124 to define one or more capability attributes, one ormore operating scenarios, one or more output goals, and one or moredesign and operating constraints. Computing device 124 may outputvarious determined combined power generation, transmission, and storagesystem design specifications, operating schedules, and power deliverystrategies to a user. Server 122 may be further divided intocoordinating server 210 and various compute servers 220, which may bedistributed across one or multiple computing devices, or implementedwithin a cloud computing environment. Server 122 of plant managementsystem 121 may be configured to perform operations to receive one ormore combined power generation, transmission, and storage systemcapability attributes, one or more operating scenarios, one or morecombined power generation, transmission, and storage system outputgoals, and one or more design and operating constraints and determine acombined power generation, transmission, and storage system designspecification, operating schedules, and power delivery strategies basedat least in part on the received one or more capability attributes, thereceived one or more operating scenarios, the received system one ormore output goals, and the received one or more design and operatingconstraints. Server 122 may have access to one or more databases 123storing data associated with historical electrical generation, powergeneration capabilities, electrical generation forecast data, bulkenergy storage capabilities, grid capabilities, historical electricityuse patterns, historical electricity pricing information, powergeneration profiles, market conditions, storage specifications, projectconstraints, or any other type information that may be suitable for useby plant management system 121. Server 122 and/or computing device 124may receive real-time data ingest 230 streams, such as, but not limitedto, current electrical generation operations, current market conditionsincluding market information 232 and market price forecasts 234, weatherforecasts 236, or any other type information that may be suitable foruse by plant management system 121. Power generation system 101 may beconstructed or otherwise configured based on the design specificationsdetermined by plant management system 121. The design specifications mayindicate optimized parameters for one or more of power generation source102, bulk energy storage system 104, and/or transmission facilities 130to which power generation system 101 may be constructed or otherwiseconfigured. Operation of plant controller 112, or plant controllers 112Aand 112B, may be monitored by plant management system 121 and theoperation of plant controller 112, or plant controllers 112A and 112B,and thereby power generation system 101, may be controlled by plantmanagement system 121.

Plant management system 121 may interface with other computing devicesconnected to network 120, such as computing device 150. Usingconnections to network 120, plant management system 121 and computingdevice 150 may exchange data with one another. Alternatively, oradditionally, computing device 150 may also directly connect to devicesof plant management system 121. Plant management system 121 mayprovision one or more interfaces to other computing devices, such ascomputing device 150, enabling the other computing devices to interactwith plant management system 121. As an example, plant management system121 may provide a market interface enabling other computing devices,such as computing device 150, to be used to buy and/or sell shortfalland/or excess power generation of power generation system 101. Thebuying/selling of shortfall/excess may be controlled by plant managementsystem 121 according to a cost strategy, such as a cost minimizingstrategy, or a value strategy, such as a value maximizing strategy, thatmay inform operation of power generation system 101, especially bulkenergy storage system 104. In this manner, bulk energy storage system104 may be operated as a hedge against volatility of market prices. Inother words, the ability of a market interface to sell and/or buy powergeneration capability through plant management system 121 may reduce thecost of supplying a load to consumers of the power from power generationsystem 101 or increase the market value of the power from powergeneration system 101 in a manner that optimizes the risk and returnprofile of the power generation system owner or operator.

Referring also to FIG. 3 , the operations of a method for managing arenewable power asset may be performed by a processor of a controldevice, such as one or more computer processors of plant managementsystem 121, of plant controller 112, of any computing device incommunication with plant management system 121 or plant controller 112,or any combination of multiple processors thereof. The method may beexecuted through software instructions executed by the processor andstored on a non-transitory processor readable or computer readablemedium. Alternatively, the functional steps of the method may beimplemented in hardware or firmware executed by the processor. Theprocessor selects 302 a renewable power asset. Selection may beautomated, or under control of a user through a user interface. Forexample, the control device may be configured to control a plurality ofrenewable power assets, and the processor may select a renewable powerasset from among the plurality of renewable power assets forconsideration.

The processor may generate forecasts of DA and RT prices via regressionor other advanced prediction techniques, which are known in the art andoutside the scope of this disclosure, against weather forecasts, supplyand demand, nodal transmission characteristics, as well as indicators ofmarket sentiment. The processor may obtain data pertaining to the nodebeing inspected including, but not limited to, historical DA and RTprices, total quantities committed at day ahead planning and total localload, historical renewable generation and day-ahead estimated renewablegeneration with associated exceedance probabilities, historicaltemperature, and plant outages. The processor may generate auxiliarysignals such as, but not limited to, on/off peak, weekday/weekend binarysignals, and season or month categorical signals. The processor may useall or a subset of the data and generate a mathematical model (such asan autoregressive-moving-average model with exogenous inputs, ARMAX, afeed-forward neural network, a recurring neural network, or similaradvanced regression models known to those skilled in the art) to predictthe DA and/or RT pricing for multiple prediction horizons. Historicaltrends of sensitivity to different processes can be extrapolated intothe future to generate multiple future scenarios with correspondingprobabilistic weighing. The processor may augment the DA and RT pricinginformation with a fundamental model based on the supply stack in theregion for additional fidelity. For example, DA price forecasts can becalculated emulating the methodology by which generation assets arecommitted to supply electricity, including the solution of linear andmixed-integer linear problems with a cost minimization target. Theprocessor may apply statistical descriptions of the model residuals toadd stochastic information back on the model output to accuratelysimulate data with properties which resemble the true signal across anumber of possible future scenarios.

After selection of the renewable power asset, the processor may obtain304 historical DA prices for energy provided from the renewable powerasset. The processor may obtain all historical DA prices, or from a dateand/or time range of historical DA prices. The processor may obtain 306historical RT prices for energy provided from the renewable power asset.The processor may obtain all historical RT prices, or from a date and/ortime range of historical RT prices. The processor may obtain a total ofDA committed energy quantities from the renewable power asset. Forexample, the processor may obtain total quantities committed at DAscheduling for the renewable power asset. The processor may obtain 310 atotal local load related to the renewable power asset. The processor mayobtain 312 historical renewable generation information and rollingforecasts related to the renewable power asset, DA estimated renewablegeneration information related to the renewable power asset, andassociated exceedance probabilities. The processor may obtain allhistorical information, or from a date and/or time range of historicalinformation. The processor may obtain 314 historical temperatureinformation and historical weather information related to the renewablepower asset. The processor may obtain all historical temperatureinformation, or from a date and/or time range of historical temperatureinformation. The processor may obtain historical renewable power assetoutage information for the renewable power asset. For example, theprocessor may obtain a date, time, season, length of outage, cause ofoutage, and other information related to historical renewable powerasset outage events. The processor may obtain 317 information related tothe regional supply stack. For example, power generation resources,including the renewable power asset, may be capable of providing aparticular quantity of energy. Further, the power generation resources,including the renewable power asset, may be capable of providing theenergy at a particular supply price (i.e., a supply offer). Theprocessor may obtain from the network a supply stack including supplyoffers from a plurality of power generation resources. The supply stackmay provide an indication of a regional cost of supplying demand forelectricity. The processor may obtain 318 demand timing information forpower demand toward which the renewable power asset may contribute. Forexample, the processor may obtain timing information such as on peak,off peak, or another demand level; demand timing according to date ofweek, weekday, weekend, and the like. The processor may obtain 320calendar information, such as power demand timing based on date, month,season, and the like; and other suitable timing information.

The processor may generate 324 a pricing prediction model for DA pricingand/or RT pricing for the renewable power asset. The pricing predictionmodel may be based on one or more of the historical DA and RT prices ofenergy, the total local load, the historical renewable generationinformation and the DA estimated renewable generation information andassociated exceedance probabilities, the historical temperatureinformation, the historical outage information, demand timinginformation, the calendar information, and the regional supply stackinformation. The generated pricing prediction model may include an ARMAXmodel, a feedforward neural network model, a recurring neural networkmodel, or similar advanced regression models known to those skilled inthe art. Based on the wide array of data inputs, the processor maygenerate the pricing prediction model and extrapolate historical trendsof sensitivity to different processes into the future, therebygenerating forward-looking scenarios with corresponding probabilisticweighing. The processor may augment the DA and RT pricing informationwith a fundamental model based on the supply stack in the region foradditional fidelity. For example, DA prices may be calculated emulatingthe methodology by which generation assets are committed to supplyelectricity, including the solution of linear and mixed-integer linearproblems with a cost minimization target. The processor may applystatistical descriptions of the model residuals to add stochasticinformation back on the model output to accurately simulate data withproperties which resemble the true signal across a number of possiblefuture scenarios. The processor may determine 326 a prediction of DAenergy prices and RT energy prices and sensitivities, meaning variationsof possible scenarios, for the renewable power asset based on thegenerated pricing prediction model.

The processor may compute 328 and place, through a market interface,optimal DA commitments for the renewable power asset, and for energyfrom any available energy storage system. For example, the processormay, in a first pass, use the forecasts of the DA and RT prices toidentify the optimal day ahead commitments of the renewable power assetacross a range of scenarios weighted by probability estimating. Equation1 is a representative value maximizing mathematical formulation for thedetermination of optimal DA commitments and expected optimal powercurtailment of a renewable power asset without any associated energystorage system.

$\begin{matrix}{\max\limits_{q_{DA},q_{X}}\{ {\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}{\gamma_{m}( {{q_{{DA}^{T}} \cdot p_{{DA},n}} + {( {q_{{RT},m} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},n,m,k}} )}}} \}} & {{Equation}1}\end{matrix}$The risk bias factor β_(m) enables a user to weigh more heavily aspecific renewable generation scenario so as to reflect asset owner riskpreferences. For example, a conservative asset owner may want to weighmore heavily a conservative estimate of renewable power output.

Equation 2 is a representative mathematical formulation that uses abalanced combination of mean and variance for the determination ofoptimal DA commitments and expected optimal power curtailment of arenewable power asset without any associated energy storage system.

$\begin{matrix}\begin{matrix}{\max\limits_{q_{DA},q_{X}}\{ {\mu_{a} - {\lambda( {{\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}{\gamma_{m}( \alpha_{n,m,k} )}^{2}}} - \mu_{\alpha}^{2}} )}} \}} \\{{Where}:} \\{\mu_{\alpha}\overset{def}{=}{\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}\gamma_{m}\alpha_{n,m,k}}}} \\{\alpha_{n,m,k}\overset{def}{=}{{q_{{DA}^{T}} \cdot p_{{DA},n}} + {( {q_{{RT},m} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},n,m,k}}}\end{matrix} & {{Equation}2}\end{matrix}$

An alternative formulation for the determination of optimal DAcommitments and expected optimal power curtailment of a renewable powerasset without any associated energy storage system, which is suited fora linear programming framework with LP computable utility functions, mayignore penalties:

_(n,m,k) −q _(DA) ^(T) ·p _(DA,n)+(q _(RT,m) −q _(DA) −q _(X))^(T) ·p_(RT,k) =q _(DA) ^(T)·(p _(DA,n) −p _(RT,k))−q _(X) ^(T) ·p _(RT,k) +q_(RT,m) ^(T) ·p _(RT,k)   Equation 3Introduce the auxiliary optimization variable

$x\overset{def}{=}\begin{bmatrix}q_{DA} \\q_{X} \\x_{{2J} + 1}\end{bmatrix}$and the unit returns

$\begin{matrix}\begin{matrix}{r_{n,k}\overset{def}{=}\begin{bmatrix}{p_{{DA},n} - p_{{RT},k}} \\{- p_{{RT},k}} \\{q_{{RT},m^{T}} \cdot p_{{RT},k}}\end{bmatrix}} & \\ & {\alpha_{n,m,k} = {{x^{T} \cdot r_{n,k}} + {q_{{RT},m^{T}} \cdot p_{{RT},k}}}}\end{matrix} & {{Equation}4}\end{matrix}$Index scenarios to t and expand to components of x, r_(t):

$\begin{matrix}{{\alpha_{t} = {{x^{T} \cdot r_{t}} = {\sum\limits_{1 \leq j \leq {{2J} + 1}}{x_{j}r_{j,t}}}}};{x_{{2J} + 1} = 1}} & {{Equation}5}\end{matrix}$Introduce average return at given time step (average across scenarios):

$\begin{matrix}{\mu_{j}\overset{def}{=}{\sum\limits_{1 \leq t \leq T}{w_{t}r_{j,t}}}} & {{Equation}6}\end{matrix}$

Different optimizations may be applied to solve the optimal DAcommitments, based on the LP goal expressed in Equation 7.

$\begin{matrix}{{\max\limits_{x}\{ {{\mu(x)} - {\lambda{\rho(x)}}} \}},{{\rho(x)}\overset{def}{=}{Dispersion}}} & {{Equation}7}\end{matrix}$Equation 8 applies a mean absolute deviation (MAD), Equation 9 applies aminimax, Equation 10 applies a conditional value at risk (CVaR),Equation 11 applies a Gini mean difference (GMD), and Equation 12applies a weighted conditional value at risk (WCVaR).

$\begin{matrix}{\max\limits_{x,d^{-}}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {\sum\limits_{t}{d_{t}^{-}w_{t}}} )}}:{d_{t}^{-} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}} \}} & {{Equation}8}\end{matrix}$ $\begin{matrix}{\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}}} \}} & {{Equation}9}\end{matrix}$ $\begin{matrix}\begin{matrix}{\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}}}}} )}}:} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}}\end{Bmatrix}} \\{{{\eta\overset{def}{=}{{VaR}{}{at}{optimum}}};{\beta\overset{def}{=}{{{probability}{of}{returns}} \leq {VaR}}}},{0 < \beta \leq 1}}\end{matrix} & {{Equation}10}\end{matrix}$ $\begin{matrix}{\max\limits_{x,u}\begin{Bmatrix}{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} )}}:} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}}{\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\ldots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\ldots T}}}}\end{Bmatrix}} & {{Equation}11}\end{matrix}$ $\begin{matrix}{\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{h}{\omega_{h}( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta_{h} + {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}} )}}:} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}}}\end{Bmatrix}} & {{Equation}12}\end{matrix}$

Alternatively, the optimization may be subject to a minimum returnconstraint as specified in Equation 13. Again, different optimizationsmay be applied, such as MAD (Equation 14), minimax (Equation 15), CVaR(Equation 16), GMD (Equation 17), or WCVaR (Equation 18).

$\begin{matrix}{\max\limits_{x}\{ {{{\mu(x)} - {{\rho(x)}:{\mu(x)}}} \geq \mu_{0}} \}} & {{Equation}13}\end{matrix}$ $\begin{matrix}{\max\limits_{x,d^{-}}\{ {{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{t}{d_{t}^{-}{w_{t}:{d_{t}^{-} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1{\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}} & {{Equation}14}\end{matrix}$ $\begin{matrix}{\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1{\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}} & {{Equation}15}\end{matrix}$ $\begin{matrix}{\max\limits_{x,d^{-},\eta}\{ {{{\eta - {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}}} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}} & {{Equation}16}\end{matrix}$ $\begin{matrix}{\max\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} + {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} ):} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\ldots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}} & {{Equation}17}\end{matrix}$ $\begin{matrix}{\max\limits_{x,d^{-}}\begin{Bmatrix}{{\sum\limits_{h}{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{{h'}t}^{-}w_{t}}}}} )}}:} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}} & {{Equation}18}\end{matrix}$

As another alternative, optimizations may be based on minimizingdispersion while subject to a minimum return constraint as specified inEquation 19. Again, different optimizations may be applied, such as MAD(Equation 20), minimax (Equation 21), CVaR (Equation 22), GMD (Equation23), or WCVaR (Equation 24).

$\begin{matrix}{\min\limits_{x}\{ {{\rho(x)}:{{\mu(x)} \geq \mu_{0}}} \}} & {{Equation}19}\end{matrix}$ $\begin{matrix}{\min\limits_{x,d^{-}}\{ {{{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}} & {{Equation}20}\end{matrix}$ $\begin{matrix}{\min\limits_{x,v}\{ {{{v:v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}} & {{Equation}21}\end{matrix}$ $\begin{matrix}{\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}{w_{t}:}}}}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}} & {{Equation}22}\end{matrix}$ $\begin{matrix}{\max\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} ):} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\ldots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}} & {{Equation}23}\end{matrix}$ $\begin{matrix}{\max\limits_{x,d^{-}}\begin{Bmatrix}{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{h}{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}}}:} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}} & {{Equation}24}\end{matrix}$

As another alternative, optimizations may be based relative to a no riskscenario as specified in Equation 25. Again, different optimizations maybe applied, such as MAD (Equation 26), minimax (Equation 27), or CVaR(Equation 28).

$\begin{matrix}{{\max\limits_{x}\{ {( {{\mu(x)} - r_{0}} )/{\rho(x)}} \}},{r_{0}\overset{def}{=}{{risk}{free}{return}}}} & {{Equation}25}\end{matrix}$ $\begin{matrix}{ {\max\limits_{\overset{\sim}{x},\overset{\sim}{d^{-}},z}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}{z:{\sum\limits_{t}{{\overset{\sim}{d}}_{t}^{-}w_{t}}}}}} = z};{{\overset{\sim}{d}}_{t}^{-} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};} \\{{{\overset{\sim}{d}}_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}} & {{Equation}26}\end{matrix}$ $\begin{matrix}{ {\max\limits_{\overset{\sim}{x},\overset{\sim}{v}}\begin{Bmatrix}{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}{z:}}} \\{{\overset{\sim}{v} = z};{\overset{\sim}{v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}} & {{Equation}27}\end{matrix}$ $\begin{matrix}{ {\max\limits_{\overset{\sim}{x},\overset{\sim}{d^{-}},z,\eta}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}{z:{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{{\overset{\sim}{d}}_{t}^{-}w_{t}}}}}}}} = z};} \\{{{\overset{\sim}{d}}_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}{\overset{\sim}{x}}_{j}}}}};{{\overset{\sim}{d}}_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}} & {{Equation}28}\end{matrix}$

As another example, the processor may, in a first pass, use theforecasts of DA and RT prices to identify the optimal day aheadcommitments of the renewable generator and a power storage asset.Optimal DA commitments, expected renewable curtailment and energystorage configuration and dispatch strategy of a renewable and storageasset may be determined based on Equation 29, subject to storage systemdischarge, charge, and state of charge constraints.

$\max\limits_{q_{DA},q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\{ {\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k\text{⁠}}{\gamma_{m}( {{q_{{DA}^{T}} \cdot p_{{DA},n}} + {( {q_{{RT},m} + d_{RT} - c_{RT} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},n,m,k} - {c_{P}P_{ESS}} - {c_{E}E_{ESS}}} )}}} \}$where

$\begin{matrix}\begin{matrix}{d_{{RT},j} \leq {P_{ESS}{\forall j}}} \\{c_{{RT},j} \leq {P_{ESS}{\forall j}}} \\{{SOC}_{j + 1} = {{SOC}_{j} - \frac{d_{{RT},j}}{\eta_{d}} + {\eta_{c}c_{{RT},j}{\forall j}}}} \\{0 \leq {SOC}_{j + 1} \leq {E_{ESS}{\forall j}}} \\{\eta_{d}\overset{def}{=}{{Discharge}{efficiency}}} \\{\eta_{c}\overset{def}{=}{{Charge}{efficiency}}}\end{matrix} & {{Equation}29}\end{matrix}$

Alternatively, as shown in Equation 30, a balanced combination of meanand variance may be used for the determination of optimal DAcommitments, expected optimal renewable curtailment and energy storageconfiguration and dispatch strategy of a renewable and storage asset.

$\begin{matrix}{{\max\limits_{q_{DA},q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\{ {\mu_{\vartheta} - {\lambda( {{\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}{\gamma_{m}( \vartheta_{n,m,k} )}^{2}}} - \mu_{\vartheta}^{2}} )}} \}}{{{where}:\mu_{\vartheta}}\overset{def}{=}{\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}\gamma_{m}\vartheta_{n,m,k}}}}{\vartheta_{n,m,k}\overset{def}{=}{{q_{{DA}^{T}} \cdot p_{{DA},n}} + {( {q_{{RT},m} + d_{RT} - c_{RT} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},n,m,k} - {c_{P}P_{ESS}} - {c_{E}E_{ESS}}}}} & {{Equation}30}\end{matrix}$

Another alternative formulation for the determination of optimal DAcommitments, expected optimal renewable curtailment and energy storageconfiguration and dispatch strategy of a renewable and storage asset,which is suited for a linear programming framework with LP computableutility functions may ignore penalties:ϑ_(n,m,k) =q _(DA) ^(T) ·p _(DA,n)+(q _(RT,m) +d _(RT) −c _(RT) −q _(DA)−q _(X))^(T) ·p _(RT,k) −c _(P) P _(ESS) −c _(E) E _(ESS) =q _(DA)^(T)·(p _(DA,n) −p _(RT,k))+d _(RT) ^(T) ·p _(RT,k) −c _(RT) ^(T) ·p_(RT,k) −q _(X) ^(T) ·p _(RT,k) +q _(RT,m) ^(T) ·p _(RT,k) −c _(P) P_(ESS) −c _(E) E _(ESS)   Equation 31Introduce the auxiliary optimization variable

$x\overset{def}{=}\begin{bmatrix}q_{DA} \\q_{X} \\d_{RT} \\c_{RT} \\P_{ESS} \\E_{ESS} \\x_{{4J} + 3}\end{bmatrix}$and the unit returns

$\begin{matrix}\begin{matrix}{r_{n,m,k}\overset{def}{=}\begin{bmatrix}{p_{{DA},n} - p_{{RT},k}} \\{- p_{{RT},k}} \\p_{{RT},k} \\{- p_{{RT},k}} \\{- c_{P}} \\{- c_{E}} \\{q_{{RT},m^{T}} \cdot p_{{RT},k}}\end{bmatrix}} & \\ & {{\vartheta_{n,m,k} = {x^{T} \cdot r_{n,m,k}}};{x_{{4J} + 3} = 1}}\end{matrix} & {{Equation}32}\end{matrix}$Index scenarios to t and expand to components of x, r_(t):

$\begin{matrix}{{\vartheta_{t} = {{x^{T} \cdot r_{t}} = {\sum\limits_{1 \leq j \leq {{4J} + 3}}{x_{j}r_{j,t}}}}};{x_{{4J} + 3} = 1}} & {{Equation}33}\end{matrix}$Introduce average return at given time step (average across scenarios):

$\begin{matrix}{\mu_{j}\overset{def}{=}{\sum\limits_{1 \leq t \leq T}{w_{t}r_{j,t}}}} & {{Equation}34}\end{matrix}$With this formulation, the same MAD, minimax, CVaR, GMD, or WCVaRoptimizations may be applied to the maximization or minimization goalsand constraints as in Equations 7-28. As it will be apparent to thoseskilled in the art, the optimizations in Equations 29-34 may be directedto calculating the expected optimal renewable curtailment and energystorage dispatch strategy of a renewable and storage asset, but not theenergy storage optimal configuration, by defining energy storage ratedpower P_(ESS) and energy storage rated energy E_(ESS) as constant andequal to the rated power and energy of a physical energy storage system.

With optimal DA commitments identified, the processor may estimate 330an optimal RT schedule for the renewable power asset. For example, in asecond pass, with the optimal day ahead commitments as fixed and the DAprices known, the processor may use the RT forecasts (e.g., with arolling horizon) to estimate an optimal RT schedule across a range ofscenarios that may be weighted by their probability. The optimal RTschedule identifies RT bids and volumes for generation. Equation 35 is avalue maximizing mathematical formulation for the determination ofoptimal RT power curtailment of a renewable power asset withoutassociated energy storage.

$\begin{matrix}{\max\limits_{q_{x}}\{ {\sum\limits_{\underset{0 \leq k \leq K}{0 \leq m \leq M}}{\overset{\sim}{w_{m,k}}{\gamma_{m}( {{( {q_{{RT},m} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},m,k}} )}}} \}} & {{Equation}35}\end{matrix}$

Alternatively, as shown in Equation 36, a balanced combination of meanand variance may be used for the determination of optimal RT powercurtailment of a renewable power asset without associated energystorage.

$\max\limits_{q_{X}}\{ {\mu_{\overset{\sim}{\alpha}} - {\lambda( {{\sum\limits_{\underset{0 \leq k \leq K}{0 \leq m \leq M}}{\gamma_{m}{()}}^{2}} - \mu_{\overset{\sim}{\alpha}}^{2}} )}} \}$where:

$\begin{matrix}\begin{matrix}{\mu_{\overset{\sim}{\alpha}}\overset{def}{=}{\sum\limits_{\underset{0 \leq k \leq K}{0 \leq m \leq M}}\gamma_{m}}} \\{\overset{def}{=}{{( {q_{{RT},m} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},m,k}}}\end{matrix} & {{Equation}36}\end{matrix}$

Another alternative formulation for the determination of optimal RTpower curtailment of a renewable power asset without associated energystorage, which is suited for a linear programming framework with LPcomputable utility functions may ignore penalties:

=(q _(RT,m) −q _(DA) −q _(X))^(T) ·p _(RT,k) =−q _(X) ^(T) ·p _(RT,k)+(q_(RT,m) −q _(DA))^(T) ·p _(RT,k)   Equation 37Introduce the auxiliary optimization variable

$x\overset{def}{=}\begin{bmatrix}q_{X} \\x_{J + 1}\end{bmatrix}$and the unit returns

$\begin{matrix}\begin{matrix}{r_{k}\overset{def}{=}\begin{bmatrix}{- p_{{RT},k}} \\{( {q_{{RT},m} - q_{DA}} )^{T} \cdot p_{{RT},k}}\end{bmatrix}} & \\ & {{= {x^{T} \cdot r_{m,k}}};{x_{J + 1} = 1}}\end{matrix} & {{Equation}38}\end{matrix}$Index scenarios to t and expand to components of x, r_(t):

$\begin{matrix}{{= {{x^{T} \cdot r_{t}} = {\sum\limits_{1 \leq j \leq {J + 1}}{x_{j}r_{j,t}}}}};{x_{J + 1} = 1}} & {{Equation}39}\end{matrix}$Introduce average return at given time step (average across scenarios):

$\begin{matrix}{\mu_{j}\overset{def}{=}{\sum\limits_{1 \leq t \leq T}{w_{t}r_{j,t}}}} & {{Equation}40}\end{matrix}$With this formulation, the same MAD, minimax, CVaR, GMD, or WCVaRoptimizations may be applied to the maximization or minimization goalsand constraints as in Equations 7-28.

As another example, the processor may estimate an optimal RT schedulefor the renewable generator and energy storage asset(s). For example, ina second pass, with the optimal day ahead commitments as fixed and theDA prices known, the processor may use the RT forecasts (e.g., with arolling horizon) to estimate optimal RT schedule across a range ofscenarios that may be weighted by their probability. Here, the optimalRT schedule identifies RT bids and volumes for generation, as well as RTbids and volumes for charging or discharging from any energy storageassets. Equation 41 is a value maximizing mathematical formulation forthe determination of optimal RT power curtailment and energy storageconfiguration and dispatch strategy of a renewable and storage asset,subject to storage system discharge, charge, and state of chargeconstraints.

$\max\limits_{q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\begin{Bmatrix}{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}{\gamma_{m}( {( {q_{{RT},m} + d_{RT} - c_{RT} - q_{DA} - q_{X}} )^{T} \cdot} }} \\ {p_{{RT},k} - \delta_{{RT},m,k} - {c_{P}P_{ESS}} - {c_{E}E_{ESS}}} )\end{Bmatrix}$where:

$\begin{matrix}{d_{{RT},j} \leq {P_{ESS}{\forall j}}} & {{Equation}41}\end{matrix}$ c_(RT, j) ≤ P_(ESS)∀j${SOC_{j + 1}} = {{SOC_{j}} - \frac{d_{{RT},j}}{\eta_{d}} + {\eta_{c}c_{{RT},j}{\forall j}}}$0 ≤ SOC_(j + 1) ≤ E_(ESS)∀j$\eta_{d}\overset{def}{=}{{Discharge}{efficiency}}$$\eta_{c}\overset{def}{=}{{Charge}{efficiency}}$

Alternatively, as shown in Equation 42, a balanced combination of meanand variance may be used for the determination of optimal RT powercurtailment and energy storage configuration and dispatch strategy of arenewable and storage asset.

$\max\limits_{q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\{ {\mu_{\overset{\sim}{\vartheta}} - {\lambda( {{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}{\gamma_{m}{()}}^{2}} - \mu_{\overset{\sim}{\vartheta}}^{2}} )}} \}$where:

$\begin{matrix}{\mu_{\overset{\sim}{\vartheta}}\overset{def}{=}{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}\gamma_{m}}} & {{Equation}42}\end{matrix}$$\overset{def}{=}{{( {q_{{RT},m} + d_{RT} - c_{RT} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},m,k} - {c_{P}P_{ESS}} - {c_{E}E_{ESS}}}$

Another alternative formulation for the determination of optimal RTpower curtailment of a renewable power asset and energy storageconfiguration and dispatch, which is suited for a linear programmingframework with LP computable utility functions may ignore penalties:

=(q _(RT,m) +d _(RT) −c _(RT) −q _(DA) −q _(X))^(T) ·p _(RT,k) −c _(P) P_(ESS) −c _(E) E _(ESS) =d _(RT) ^(T) ·p _(RT,k) −c _(RT) ^(T) ·p_(RT,k) −q _(X) ^(T) ·p _(RT,k)+(q _(RT,m) −q _(DA))^(T) ·p _(RT,k) −c_(P) P _(ESS) −c _(E) E _(ESS)   Equation 43Introduce the auxiliary optimization variable

$x\overset{def}{=}\begin{bmatrix}q_{X} \\d_{RT} \\c_{RT} \\P_{ESS} \\E_{ESS} \\x_{{3J} + 3}\end{bmatrix}$and the unit returns

$r_{k}\overset{def}{=}\begin{bmatrix}{- p_{{RT},k}} \\p_{{RT},k} \\{- p_{{RT},k}} \\{- c_{P}} \\{- c_{E}} \\{( {q_{{RT},m} - q_{DA}} )^{T} \cdot p_{{RT},k}}\end{bmatrix}$

=x ^(T) ·r _(m,k) ;x _(3J+3)=1   Equation 44

Index scenarios to t and expand to components of x, r_(t):

$\begin{matrix}{{= {{x^{T} \cdot r_{t}} = {\sum\limits_{1 \leq j \leq {{3J} + 3}}{x_{j}r_{j,t}}}}};{x_{{3J} + 3} = 1}} & {{Equation}45}\end{matrix}$Introduce average return at given time step (average across scenarios):

$\begin{matrix}{\mu_{j}\overset{def}{=}{\sum\limits_{1 \leq t \leq T}{w_{t}r_{j,t}}}} & {{Equation}46}\end{matrix}$With this formulation, the same MAD, minimax, CVaR, GMD, or WCVaRoptimizations may be applied to the maximization or minimization goalsand constraints as in Equations 7-28. As it will be apparent to thoseskilled in the art, the optimizations in Equations 41-46 may be directedto calculating the expected optimal renewable curtailment and energystorage dispatch strategy of a renewable and storage asset, but not theenergy storage optimal configuration, by defining energy storage ratedpower P_(ESS) and energy storage rated energy E_(ESS) as constant andequal to the rated power and energy of a physical energy storage system.

As another example, the processor may determine optimal RT renewablecurtailment and of energy storage and other generation assets dispatchstrategy through cost minimization as shown in Equation 47, subject tostorage system discharge, charge, and state of charge constraints.

$\min\limits_{q_{X},d_{RT},c_{RT}}\{ {\sum\limits_{\substack{0 \leq i \leq I \\ 0 \leq m \leq M}}{w_{i,m}{\gamma_{m}( {( {l_{{RT},i} - q_{{RT},m} - d_{RT} + c_{RT} + q_{X}} )^{T} \cdot c_{{RT},i,m}} )}}} \}$where:

$\begin{matrix}{d_{{RT},j} \leq {P_{ESS}{\forall j}}} & {{Equation}47}\end{matrix}$ c_(RT, j) ≤ P_(ESS)∀j${SOC_{j + 1}} = {{SOC_{j}} - \frac{d_{{RT},j}}{\eta_{d}} + {\eta_{c}c_{{RT},j}{\forall j}}}$0 ≤ SOC_(j + 1) ≤ E_(ESS)∀j$\eta_{d}\overset{def}{=}{{Discharge}{efficiency}}$$\eta_{c}\overset{def}{=}{{Charge}{efficiency}}$

Equations 1-47 present examples of optimization frameworks which may beimplemented in software for operation by the processor and may be solvedthrough known linear and non-linear mathematical programming techniques.

The processor may leverage mathematical methods (e.g. mean-varianceoptimization) to compute the optimal DA and RT strategy that reflectsthe preferences of the asset owner of risk-adjusted return. The RTstrategy may deliver a renewable RT schedule and incurring penalties attimes when RT schedule is short of the DA commitments. The RT strategymay buy power from RT markets to fill DA commitments at times when RTschedule is short of such commitments.

With optimal DA and RT strategy determined, the processor may obtain 336real power prices from the communication network. For example, theprocessor may obtain the real power prices from another network element,such as a network element associated with one or more energy pricingmarkets. The processor may evaluate 338 the RT bids and volume strategyto refine the optimization operations based on the obtained real powerprices. For example, the processor may evaluate optimal DA and RTstrategy against a back-test of real prices to refine the optimaloperation further, for example by training a neural network to minimizethe error between the optimal DA and RT strategy based on DA and RTprice forecasts and the optimal DA and RT strategy based on DA and RTrealized prices and applying the network itself to correct for futureoptimal DA and RT strategy. With the strategy refined based on RTpricing, the processor may participate in a Real Time energy market anddeliver volumes of energy based on the RT price as determined by the RTstrategy. The processor may control the renewable power asset and anyassociated energy storage to deliver 342 committed power based on anyexisting DA commitments and the refined RT delivery strategy.

The various optimization frameworks detailed above may be implementedindividually for a specific system and owner. For example, a specificlong-term expected profit maximization optimization may be selected andimplemented in software for an owner or operator of a renewablegeneration asset with no related storage in a deregulated Day Ahead/RealTime energy market. A different cost minimization optimization may beimplemented in software for an owner or operator of a renewablegeneration asset with storage in a regulated environment. Thus thesoftware, and operation of the processor, may be customized to eachsystem, owner, and owner preferences. Alternatively, multipleoptimization frameworks may be implemented in software. Owner oroperator selection between different optimizations may be in advancethrough a user interface selection or configuration setting, or multipleoptimizations may be run and then selected between for ongoing systemoperation.

In an alternate embodiment, the optimizations may be used separately indesign or expansion stages. Software may be run on the same processorthat may be used in system operation, or also on a separate processor ofa computing device for modeling the design or expansion of a powergeneration system. For example, the optimizations may be used todetermine scale of a new generation asset and new related storage asset,or to determine scale of a new storage asset to add to an existinggeneration asset. The processor may estimate the optimal renewable andstorage asset size by performing simulations that assume a givenrenewable and storage asset configuration and calculate the totalrevenues or cost savings accrued during an asset lifetime, according toany of Equations 1, 2, 29, 30, 35, 36, 41, 42, or 47. Historical orsynthetic renewable generation and forecast scenarios may be used in thesimulations. Synthetic renewable generation and forecast scenarios maybe produced by sampling from the statistics of historical scenarios andby solving meteorological models so as to be consistent with thefundamental phenomena. Historical or synthetic DA and RT electricitymarket price scenarios (realized and forecasted) may be used in thesimulations. Synthetic DA and RT electricity market price scenarios maybe produced by sampling from the statistics of historical scenarios andby solving unit commitment and dispatch models so as to be consistentwith the fundamental phenomena. The optimal renewable and storage assetsize may be the one that delivers the highest asset owner returns asmeasured by well-known metrics such as, but not limited to,risk-adjusted internal rate of return or return on investment. Theprocessor may estimate the optimal renewable and storage asset size bycomparing the cost of different amounts of renewable and storage withthe risk-adjusted benefit (value increase or cost reduction) generatedover an expected lifetime of the system.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope should, therefore, be determined with referenceto the appended claims, along with the full scope of equivalents towhich such claims are entitled.

What is claimed is:
 1. A method to manage operation of a renewable powergeneration asset, comprising: operating one or more computing devicescontrolling or in communication with one or more control devices of arenewable power generator; operating the renewable power generatorindependent from any bulk energy storage systems; obtaining, by the oneor more computing devices, historical data pertinent to the renewablepower generator as a node within an electrical supply grid, wherein theelectrical supply grid is within a market price environment; generating,by the one or more computing devices and based on the obtainedhistorical data, forecasts of Day Ahead (DA) and Real Time (RT) prices;identifying, by the one or more computing devices, optimal DA powercommitments of the renewable power generator across a range of scenariosweighted by probability and by applying one of a maximizing formulation,a balanced combination of mean and variance, or a linear programmingframework based on utility functions, wherein: q_(DA) is a day aheadcommitment; q_(x) is a real time curtailment of renewable generation;q_(RT) is a real time renewable generation; p_(DA) is a realized dayahead price; p_(RT) is a real time price; N is a number of possible DAprice scenarios, indexed by 1≤n≤N; M is a number of possible renewablegeneration scenarios, indexed by 1≤m≤M; K is a number of possible RTprice scenarios, indexed by 1≤k≤K; q_(RT,m) is a real time renewablegeneration forecast in an m^(th) scenario; p_(DA,n) is a day ahead priceforecast in an n^(th) scenario; p_(RT,k) is a real time price forecastin a k^(th) scenario; w_(n,m,k) is a joint probability of(p_(DA)=p_(DA,n); q_(RT)=q_(RT,m); p_(RT)=p_(RT,k)); γ_(m) is a factorbiasing optimization towards specific generation forecast scenarios;δ_(RT,n,m,k) is a function of deviation from day ahead commitments andmarket prices, and represents real time penalties;${\mu_{\alpha}\overset{def}{=}{\sum\limits_{\substack{1 \leq n \leq N \\ 1 \leq m \leq M \\ 1 \leq k \leq K}}{w_{n,m,k}\gamma_{m}\alpha_{n,m,k}}}};$

_(n,m,k)

q _(DA) ^(T) ·p _(DA,n)+(q _(RT,m) −q _(DA) −q _(X))^(T) ·p_(RT,k)−δ_(RT,n,m,k); applying a maximizing formulation maximizes${\max\limits_{q_{DA},q_{X}}\{ {\sum\limits_{\substack{1 \leq n \leq N \\ 1 \leq m \leq M \\ 1 \leq k \leq K}}{w_{n,m,k}{\gamma_{m}( {{q_{DA}^{T} \cdot p_{{DA},n}} + {( {q_{{RT},m} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},n,m,k}} )}}} \}};$applying a balanced combination of mean and variance maximizes${\max\limits_{q_{DA},q_{X}}\{ {\mu_{\alpha} - {\lambda( {{\sum\limits_{\substack{1 \leq n \leq N \\ 1 \leq m \leq M \\ 1 \leq k \leq K}}{w_{n,m,k}{\gamma_{m}( \alpha_{n,m,k} )}^{2}}} - \mu_{\alpha}^{2}} )}} \}};$ and applying a linear programming framework based on utility functionsoptimizes through one of maximizing utility, maximizing utility subjectto a minimum return constraint, minimizing dispersion while subject to aminimum return constraint, or maximizing utility relative to a no riskscenario, where:μ_(j)

Σ_(1≤t≤T) w _(t) r _(j,t),

_(t) =x ^(T) ·r _(t)=Σ_(1≤j≤2J+1) x _(j) r _(j,t) ;x _(2J+1)=1,${x\overset{def}{=}\begin{bmatrix}q_{DA} \\q_{X} \\x_{{2J} + 1}\end{bmatrix}},$ ${r_{n,k}\overset{def}{=}\begin{bmatrix}{p_{{DA},n} - p_{{RT},k}} \\{- p_{{RT},k}} \\{q_{{RT},m}^{T} \cdot p_{{RT},k}}\end{bmatrix}},$ λ is a risk tolerance factor, maximizing utility uses alinear programming goal${\max\limits_{x}\{ {{\mu(x)} - {\lambda{\rho(x)}}} \}},{{\rho(x)}\overset{def}{=}{Dispersion}},$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {\sum\limits_{t}{d_{t}^{-}w_{t}}} )}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}} \}},$a minimax through:${\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}}} \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}}}}} )}:}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}}\end{Bmatrix}},$  with η

VaR at optimum and β

probability of returns≤VaR, 0<β≤1, a Gini mean difference:${\max\limits_{x,u}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\substack{t \\ j}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\substack{1 \leq t^{\prime} \leq {T - 1} \\ {t^{\prime} + 1} \leq t^{''} \leq T}}{u_{t^{\prime},t^{''}}w_{t^{\prime}}w_{t^{''}}}}}} )}:}} \\{{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{''}}x_{j}}}};{{\forall t^{\prime}} =}} \\{{{1\ldots T} - 1};{t^{''} = {t^{\prime} + {1\ldots T}}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{\sum\limits_{h}{{\omega_{h}( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta_{h} + {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}} )}:}} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}}}\end{Bmatrix}};$ maximizing utility subject to a minimum returnconstraint uses a linear programming goal$\max\limits_{x}\{ {{{\mu(x)} - {{\rho(x)}:{\mu(x)}}} \geq \mu_{0}} \}$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a minimax through:${\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\{ {{{\eta - {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}}} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a Gini mean difference: ${\max\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{\substack{t \\ j}}{r_{j,t}x_{j}w_{t}^{2}}} + {2{\sum\limits_{\substack{1 \leq t^{\prime} \leq {T - 1} \\ {t^{\prime} + 1} \leq t^{''} \leq T}}{u_{t^{\prime},t^{''}}w_{t^{\prime}}w_{t^{''}}}}}} ):} \\{{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{''}}x_{j}}}};{{\forall t^{\prime}} =}} \\{{{1\ldots T} - 1};{t^{''} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{\sum\limits_{h}{{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}:}} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} =}} \\{{1\ldots T};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}};$ minimizing dispersion while subject to a minimum returnconstraint uses a linear programming goal$\min\limits_{x}\{ {{{\rho(x)}:{\mu(x)}} \geq \mu_{0}} \}$ and applies one of: a mean absolute deviation through:${\min\limits_{x,d^{-}}\{ {{{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a minimax through:${\min\limits_{x,v}\{ {{{v:v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\min\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:}}}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j.t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ a Gini mean difference:${\min\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\substack{t \\ j}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\substack{1 \leq t^{\prime} \leq {T - 1} \\ {t^{\prime} + 1} \leq t^{''} \leq T}}{u_{t^{\prime},t^{''}}w_{t^{\prime}}w_{t^{''}}}}}} ):} \\{{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{''}}x_{j}}}};{{\forall t^{\prime}} =}} \\{{{1\ldots T} - 1};{t^{''} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\min\limits_{x,d^{-}}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{h}{{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}:}}} \\\begin{matrix}{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} =}} \\{{1\ldots T};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{matrix}\end{Bmatrix}};$ and maximizing utility relative to a no risk scenariouses a linear programming goal${\max\limits_{x}\{ {( {{\mu(x)} - r_{0}} )/{\rho(x)}} \}},{r_{0}\overset{def}{=}{{risk}{free}{return}}}$ and applies one of: a mean absolute deviation through:${ {\max\limits_{\overset{\sim}{x},{\overset{\sim}{d}}^{-},z}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:{\sum\limits_{t}{\overset{\sim}{d_{t}^{-}}w_{t}}}}} = z};{\overset{\sim}{d_{t}^{-}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};} \\{{\overset{\sim}{d_{t}^{-}} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}},$ a minimaxthrough:${ {\max\limits_{\overset{\sim}{x},\overset{\sim}{v}}\begin{Bmatrix}{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:}} \\\begin{matrix}{{\overset{\sim}{v} = z};{\overset{\sim}{v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};{{\forall t} =}} \\{{1\ldots T};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{matrix}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}},$ aconditional value at risk through:${ {\max\limits_{\overset{\sim}{x},{\overset{\sim}{d}}^{-},z,\eta}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{\overset{\sim}{d_{t}^{-}}w_{t}}}}} = z};} \\{{\overset{\sim}{d_{t}^{-}} \geq {\eta - {\sum\limits_{j}{r_{j,t}{\overset{\sim}{x}}_{j}}}}};{\overset{\sim}{d_{t}^{-}} \geq 0};{{\forall t} =}} \\{{1\ldots T};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}};$estimating, by the one or more computing devices, RT schedules forgeneration by the renewable power generator across a range of scenariosweighted by probability; applying, by the one or more computing devices,a linear programming framework factoring risk and return preferences ofan owner or operator of the renewable power generator, and computing anoptimal DA and RT strategy; controlling, by the one or more computingdevices or the one or more control devices in communication with the oneor more computing devices, the renewable power generation; placing, bythe one or more computing devices through a market interface to a dayahead energy market, optimal DA commitments for the renewable powergenerator; and generating and delivering power to the power grid basedon the optimal DA and RT strategy or according to the optimal RTgeneration schedule.
 2. A method to manage operation of a renewablepower generation asset, comprising: operating one or more computingdevices controlling or in communication with one or more control devicesof a renewable power generator; operating the renewable power generatorindependent from any bulk energy storage systems; obtaining, by the oneor more computing devices, historical data pertinent to the renewablepower generator as a node within an electrical supply grid, wherein theelectrical supply grid is within a market price environment; generating,by the one or more computing devices and based on the obtainedhistorical data, forecasts of Day Ahead (DA) and Real Time (RT) prices;identifying, by the one or more computing devices, optimal DA powercommitments of the renewable power generator across a range of scenariosweighted by probability; estimating, by the one or more computingdevices, RT schedules for generation by the renewable power generatoracross a range of scenarios weighted by probability and by applying oneof a maximizing formulation, a balanced combination of mean andvariance, or a linear programming framework based on utility functions,wherein: q_(DA) is a day ahead commitment; q_(X) is a real timecurtailment of renewable generation; q_(RT) is a real time renewablegeneration; p_(DA) is a realized day ahead price; p_(RT) is a real timeprice; N is a number of possible DA price scenarios, indexed by 1≤n≤N; Mis a number of possible renewable generation scenarios, indexed by1≤m≤M; K is a number of possible RT price scenarios, indexed by 1≤k≤K;q_(RT,m) is a real time renewable generation forecast in an m^(th)scenario; p_(DA,n) is a day ahead price forecast in an n^(th) scenario;p_(RT,k) is a real time price forecast in a k^(th) scenario; w_(n,m,k)is a joint probability of (p_(DA)=p_(DA,n); q_(RT)=q_(RT,m);p_(RT)=p_(RT,k)); γ_(m) is a factor biasing optimization towardsspecific generation forecast scenarios; δ_(RT,n,m,k) is a function ofdeviation from day ahead commitments and market prices, and representsreal time penalties;

is a joint probability of (q_(RT)=q_(RT,m); p_(RT)=p_(RT,k)) given arealized p_(DA);${\mu_{\overset{\sim}{\alpha}}\overset{def}{=}{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}\gamma_{m}}};$

(q _(RT,m) −q _(DA) −q _(X))^(T) ·p _(RT,k)−δ_(RT,m,k); λ is a risktolerance factor; applying a maximizing formulation maximizes${\max\limits_{q_{X}}\{ {\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}{\gamma_{m}( {{( {q_{{RT},m} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},m,k}} )}} \}};$applying a balanced combination of mean and variance maximizes${\max\limits_{q_{X}}\{ {\mu_{\overset{\sim}{\alpha}} - {\lambda( {{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}{\gamma_{m}{()}}^{2}} - \mu_{\overset{\sim}{\alpha}}^{2}} )}} \}};$ and applying a linear programming framework based on utility functionsoptimizes through one of maximizing utility, maximizing utility subjectto a minimum return constraint, minimizing dispersion while subject to aminimum return constraint, or maximizing utility relative to a no riskscenario, where:μ_(j)

Σ_(1≤t≤T) w _(t) r _(j,t),

=x ^(T) ·r _(t)=Σ_(1≤j≤J+1) x _(j) r _(j,t) ;x _(J+1)=1,${x\overset{def}{=}\begin{bmatrix}q_{X} \\x_{J + 1}\end{bmatrix}},$ ${r_{k}\overset{def}{=}\begin{bmatrix}{- p_{{RT},k}} \\{( {q_{{RT},m} - q_{DA}} )^{T} \cdot p_{{RT},k}}\end{bmatrix}},$ maximizing utility uses a linear programming goal${\max\limits_{x}\{ {{\mu(x)} - {\lambda{\rho(x)}}} \}},{{\rho(x)}\overset{def}{=}{Dispersion}},$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {\sum\limits_{t}{d_{t}^{-}w_{t}}} )}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}} \}},$a minimax through:${\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}}} \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}}}}} )}:}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}}\end{Bmatrix}},$  with η

VaR at optimum and β

probability of returns≤VaR, 0<β≤1, a Gini mean difference:${\max\limits_{x,u}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\substack{t \\ j}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\substack{1 \leq t^{\prime} \leq {T - 1} \\ {t^{\prime} + 1} \leq t^{''} \leq T}}{u_{t^{\prime},t^{''}}w_{t^{\prime}}w_{t^{''}}}}}} )}:}} \\\begin{matrix}{{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{''}}x_{j}}}};{{\forall t^{\prime}} =}} \\{{{1\ldots T} - 1};{t^{''} = {t^{\prime} + {1\ldots T}}}}\end{matrix}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{{\sum\limits_{h}{\omega_{h}( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta_{h} + {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}} )}}:} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}}}\end{Bmatrix}};$ maximizing utility subject to a minimum returnconstraint uses a linear programming goal$\max\limits_{x}\{ {{{\mu(x)} - {{\rho(x)}:{\mu(x)}}} \geq \mu_{0}} \}$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a minimax through:${\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\{ {{{\eta - {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}}} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a Gini mean difference: ${\max\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} + {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} ):} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\cdots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\cdots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{{\sum\limits_{h}{\omega_{h}( {\eta_{h} + {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}}:} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}};$ minimizing dispersion while subject to a minimum returnconstraint uses a linear programming goal$\min\limits_{x}\{ {{{\rho(x)}:{\mu(x)}} \geq \mu_{0}} \}$ and applies one of: a mean absolute deviation through:${\min\limits_{x,d^{-}}\{ {{{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a minimax through:${\min\limits_{x,v}\{ {{{v:v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\min\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}{w_{t}:}}}}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ a Gini mean difference:${\min\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} ):} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\cdots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\cdots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\min\limits_{x,d^{-}}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}\underset{h}{- \sum}{\omega_{h}( {\eta_{h} + {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}}}:} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}};$ and maximizing utility relative to a no risk scenariouses a linear programming goal${\max\limits_{x}\{ {( {{\mu(x)} - r_{0}} )/{\rho(x)}} \}},{r_{0}\overset{def}{=}{{risk}{free}{return}}}$ and applies one of: a mean absolute deviation through:${ {\max\limits_{\overset{\sim}{x},{\overset{\sim}{d}}^{-},z}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}{z:{\sum\limits_{t}{{\overset{\sim}{d}}_{t}^{-}w_{t}}}}}} = z};{{\overset{\sim}{d}}_{t}^{-} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};} \\{{{\overset{\sim}{d}}_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}},$ a minimaxthrough:${ {\max\limits_{\overset{\sim}{x},\overset{\sim}{v}}\begin{Bmatrix}{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:}} \\{{\overset{\sim}{v} = z};{\overset{\sim}{v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}},$ aconditional value at risk through:${ {\max\limits_{\overset{\sim}{x},{\overset{\sim}{d}}^{-},z,\eta}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}{z:{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{{\overset{\sim}{d}}_{t}^{-}w_{t}}}}}}}} = z};} \\{{{\overset{\sim}{d}}_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}{\overset{\sim}{x}}_{j}}}}};{{\overset{\sim}{d}}_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}};$ applying,by the one or more computing devices, a linear programming frameworkfactoring risk and return preferences of an owner or operator of therenewable power generator, and computing an optimal DA and RT strategy;controlling, by the one or more computing devices or the one or morecontrol devices in communication with the one or more computing devices,the renewable power generation; placing, by the one or more computingdevices through a market interface to a day ahead energy market, optimalDA commitments for the renewable power generator; and generating anddelivering power to the power grid based on the optimal DA and RTstrategy or according to the optimal RT generation schedule.
 3. A methodto manage operation of a renewable power generation asset, comprising:operating one or more computing devices controlling or in communicationwith one or more control devices of a renewable power generator;controlling operation, by the one or more computing devices, of one ormore bulk energy storage systems in association with the renewable powergenerator, each of the one or more bulk energy storage systems being ofshort duration energy storage or of long duration energy storage;obtaining, by the one or more computing devices, historical datapertinent to the renewable power generator as a node within anelectrical supply grid, wherein the electrical supply grid is within amarket price environment; generating, by the one or more computingdevices and based on the obtained historical data, forecasts of DayAhead (DA) and Real Time (RT) prices; identifying, by the one or morecomputing devices, optimal DA power commitments of the renewable powergenerator across a range of scenarios weighted by probability and byapplying one of a maximizing formulation, a balanced combination of meanand variance, or a linear programming framework based on utilityfunctions, wherein: q_(DA) is a day ahead commitment; q_(x) is a realtime curtailment of renewable generation; q_(RT) is a real timerenewable generation; p_(DA) is a realized day ahead price; p_(RT) is areal time price; c_(RT) is a RT optimal charge schedule of a storagesystem; c_(p) is a unit power cost of the storage system amortized in anoptimization horizon; c_(E) is a unit energy cost of the storage systemamortized in the optimization horizon; d_(RT) is a RT optimal dischargeschedule of the storage system; P_(ESS) is a rated power of the storagesystem; E_(ESS) is a rated energy of the storage system; N is a numberof possible DA price scenarios, indexed by 1≤n≤N; M is a number ofpossible renewable generation scenarios, indexed by 1≤m≤M; K is a numberof possible RT price scenarios, indexed by 1≤k≤K; q_(RT,m) is a realtime renewable generation forecast in an m^(th) scenario; p_(DA,n) is aday ahead price forecast in an n^(th) scenario; p_(RT,k) is a real timeprice forecast in a k^(th) scenario; w_(n,m,k) is a joint probability of(p_(DA)=p_(DA,n); q_(RT)=q_(RT,m); P_(RT)=p_(RT,k)); γ_(m) is a factorbiasing optimization towards specific generation forecast scenarios;δ_(RT,n,m,k) is a function of deviation from day ahead commitments andmarket prices, and represents real time penalties;${\mu_{\vartheta}\overset{def}{=}{\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}\gamma_{m}\vartheta_{n,m,k}}}};$ϑ_(n,m,k)

q _(DA) ^(T) ·p _(DA,n)+(q _(RT,m) +d _(RT) −c _(RT) −q _(DA) −q_(X))^(T) ·p _(RT,k)−δ_(RT,n,m,k) −c _(P) P _(ESS) −c _(E) E _(ESS);η_(d)

Discharge efficiency; η_(c)

Charge efficiency; λ is a risk tolerance factor; applying a maximizingformulation maximizes${{\max\limits_{q_{DA},q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\{ {\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}{\gamma_{m}( {{q_{{DA}^{T}} \cdot p_{{DA},n}} + \text{ }{( {q_{{RT},m} + d_{RT} - c_{RT} - q_{DA} - q_{X}} )^{T} \cdot p_{{RT},k}} - \delta_{{RT},n,m,k} - \text{ }{c_{p}P_{ESS}} - {c_{E}E_{ESS}}} )}}} \}{where}d_{{RT},j}} \leq {P_{ESS}{\forall j}}},{c_{{RT},j} \leq {P_{ESS}{\forall j}}},{{SOC}_{j + 1} = {{SOC_{j}} - \frac{d_{{RT},j}}{\eta_{d}} + {\eta_{c}c_{{RT},j}{\forall j}}}},{{0 \leq {SOC}_{j + 1} \leq {E_{ESS}{\forall j}}};}$applying a balanced combination of mean and variance maximizes${\max\limits_{q_{DA},q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\{ {\mu_{\vartheta} - {\lambda( {{\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}{\gamma_{m}( \vartheta_{n,m,k} )}^{2}}} - \mu_{\vartheta}^{2}} )}} \}};$ and applying a linear programming framework based on utility functionsoptimizes through one of maximizing utility, maximizing utility subjectto a minimum return constraint, minimizing dispersion while subject to aminimum return constraint, or maximizing utility relative to a no riskscenario, where:μ_(j)

Σ_(1≤t≤T) w _(t) r _(j,t),ϑ_(t) =x ^(T) ·r _(t)=Σ_(1≤j≤4J+3) x _(j) r _(j,t) ;x _(4J+3)=1,${x\overset{def}{=}\begin{Bmatrix}q_{DA} \\q_{X} \\d_{RT} \\c_{RT} \\P_{ESS} \\E_{ESS} \\x_{{4J} + 3}\end{Bmatrix}},{r_{n,m,k}\overset{def}{=}\begin{Bmatrix}{p_{{DA},n} - p_{{RT},k}} \\{- p_{{RT},k}} \\p_{{RT},k} \\{- p_{{RT},k}} \\{- c_{P}} \\{- c_{E}} \\{q_{{RT},m^{T}} \cdot p_{{RT},k}}\end{Bmatrix}},$ maximizing utility uses a linear programming goal${\max\limits_{x}\{ {{\mu(x)} - {\lambda{\rho(x)}}} \}},{{\rho(x)}\overset{def}{=}{Dispersion}},$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\{ {{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {\sum\limits_{t}{d_{t}^{-}w_{t}}} )}}:{d_{t}^{-} \geq ( {{\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}} }} \}},$a minimax through:${\max\limits_{x,v}\{ {{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda v}:v}} \geq ( {{\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}};{{\forall t} = {1\ldots T}}} } \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}}}}} )}}:} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}}\end{Bmatrix}},$  with η

VaR at optimum and β

probability of returns≤VaR, 0<β≤1, a Gini mean difference:${\max\limits_{x,u}\begin{Bmatrix}{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} )}}:} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\ldots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\ldots T}}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{{\sum\limits_{h}{\omega_{h}( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta_{h} + {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}} )}}:} \\{{d_{t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}}}\end{Bmatrix}};$ maximizing utility subject to a minimum returnconstraint uses a linear programming goal$\max\limits_{x}\{ {{{\mu(x)} - {\rho(x)}}:{{\mu(x)} \geq \mu_{0}}} \}$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a minimax through:${\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\{ {{{\eta - {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}}} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a Gini mean difference: ${\max\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} + {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} ):} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\ldots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{{E_{h}{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}}:} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}};$ minimizing dispersion while subject to a minimum returnconstraint uses a linear programming goal$\min\limits_{x}\{ {{{\rho(x)}:{\mu(x)}} \geq \mu_{0}} \}$ and applies one of: a mean absolute deviation through:${\min\limits_{x,d^{-}}\{ {{{{{{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};}d_{t}^{-}} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a minimax through:${\min\limits_{x,v}\{ {{{v:v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\min\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:}}}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ a Gini mean difference:${\min\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\underset{j}{t}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\underset{{t^{\prime} + 1} \leq t^{\prime\prime} \leq T}{1 \leq t^{\prime} \leq {T - 1}}}{u_{t^{\prime},t^{\prime\prime}}w_{t^{\prime}}w_{t^{\prime\prime}}}}}} ):} \\{{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{\prime\prime}} \leq {\sum\limits_{j}{r_{j,t^{\prime\prime}}x_{j}}}};{{\forall t^{\prime}} = {{1\ldots T} - 1}};{t^{\prime\prime} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\min\limits_{x,d^{-}}\begin{Bmatrix}{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{h}{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}}}:} \\{{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0}},{{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}}\end{Bmatrix}};$ and maximizing utility relative to a no risk scenariouses a linear programming goal${\max\limits_{x}\{ {( {{\mu(x)} - r_{0}} )/{\rho(x)}} \}},{r_{0}\overset{def}{=}{{risk}{free}{return}}}$ and applies one of: a mean absolute deviation through: max x ~ ⁢ d ~ - ,z { ∑ j x ~ j ⁢ μ j - r 0 ⁢ z : ∑ t d ~ t - ⁢ w t = z ; d ~ t - ≥ ∑ j ( μj - r j , t ) ⁢ x ~ j ; d ~ t - ≥ 0 ; ∀ t = 1 ⁢ … ⁢ T ; ∑ j x ~ j = z ; x ~j ≥ 0 } ⇒ x = x ~ / z , a minimax through:${ {\max\limits_{\overset{\sim}{x},\overset{\sim}{v}}\begin{Bmatrix}{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:}} \\{{\overset{\sim}{v} = z};{\overset{\sim}{v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}},$ aconditional value at risk through:${ {\max\limits_{\overset{\sim}{x},{\overset{\sim}{d}}^{-},z,\eta}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{{\overset{\sim}{d}}_{t}^{-}w_{t}}}}} = z};} \\{{{\overset{\sim}{d}}_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}{\overset{\sim}{x}}_{j}}}}};{{\overset{\sim}{d}}_{t}^{-} \geq 0};{\forall_{t}{= {1\ldots T}}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}};$estimating, by the one or more computing devices, RT schedules forgeneration by the renewable power generator across a range of scenariosweighted by probability; applying, by the one or more computing devices,a linear programming framework factoring risk and return preferences ofan owner or operator of the renewable power generator, and computing anoptimal DA and RT strategy; controlling, by the one or more computingdevices or the one or more control devices in communication with the oneor more computing devices, the renewable power generation; placing, bythe one or more computing devices through a market interface to a dayahead energy market, optimal DA commitments for the renewable powergenerator; and generating and delivering power to the power grid basedon the optimal DA and RT strategy or according to the optimal RTgeneration schedule.
 4. A method to manage operation of a renewablepower generation asset, comprising: operating one or more computingdevices controlling or in communication with one or more control devicesof a renewable power generator; controlling operation, by the one ormore computing devices, of one or more bulk energy storage systems inassociation with the renewable power generator, each of the one or morebulk energy storage systems being of short duration energy storage or oflong duration energy storage; obtaining, by the one or more computingdevices, historical data pertinent to the renewable power generator as anode within an electrical supply grid, wherein the electrical supplygrid is within a market price environment; generating, by the one ormore computing devices and based on the obtained historical data,forecasts of Day Ahead (DA) and Real Time (RT) prices; identifying, bythe one or more computing devices, optimal DA power commitments of therenewable power generator across a range of scenarios weighted byprobability; estimating, by the one or more computing devices, RTschedules for generation by the renewable power generator across a rangeof scenarios weighted by probability and by applying one of a maximizingformulation, a balanced combination of mean and variance, or a linearprogramming framework based on utility functions, wherein: q_(DA) is aday ahead commitment; q_(X) is a real time curtailment of renewablegeneration; q_(RT) is a real time renewable generation; p_(DA) is arealized day ahead price; p_(RT) is a real time price; c_(RT) is a RToptimal charge schedule of a storage system; c_(p) is a unit power costof the storage system amortized in an optimization horizon; c_(E) is aunit energy cost of the storage system amortized in the optimizationhorizon; d_(RT) is a RT optimal discharge schedule of the storagesystem; P_(ESS) is a rated power of the storage system; E_(ESS) is arated energy of the storage system; N is a number of possible DA pricescenarios, indexed by 1≤n≤N; M is a number of possible renewablegeneration scenarios, indexed by 1≤m≤M; K is a number of possible RTprice scenarios, indexed by 1≤k≤K; q_(RT,m) is a real time renewablegeneration forecast in an m^(th) scenario; p_(DA,n) is a day ahead priceforecast in an n^(th) scenario; p_(RT,k) is a real time price forecastin a k^(th) scenario; w_(n,m,k) is a joint probability of(p_(DA)=p_(DA,n); q_(RT)=q_(RT,m); p_(RT)=p_(RT,k)); γ_(m) is a factorbiasing optimization towards specific generation forecast scenarios;δ_(RT,n,m,k) is a function of deviation from day ahead commitments andmarket prices, and represents real time penalties;${{\mu\vartheta}\overset{def}{=}{\sum\limits_{\underset{\underset{1 \leq k \leq K}{1 \leq m \leq M}}{1 \leq n \leq N}}{w_{n,m,k}\gamma_{m}\vartheta_{n,m,k}}}};$ϑ_(n,m,k)

q _(DA) ^(T) ·p _(DA,n)+(q _(RT,m) +d _(RT) −c _(RT) −q _(DA) −q_(X))^(T) ·p _(RT,k)−δ_(RT,n,m,k) −c _(P) P _(ESS) −c _(E) E _(ESS);η_(d)

Discharge efficiency; η_(c)

Charge efficiency; λ is a risk tolerance factor; applying a maximizingformulation maximizes$\max\limits_{q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\begin{Bmatrix}{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}{\gamma_{m}( {( {q_{{RT},m} + d_{RT} - c_{RT} - q_{DA} - q_{X}} )^{T} \cdot} }} \\ {p_{{RT},k} - \delta_{{RT},m,k} - {c_{P}P_{ESS}} - {c_{E}E_{ESS}}} )\end{Bmatrix}$  where:d _(RT,j) ≤P _(ESS)∀_(j),c _(RT,j) ≤P _(ESS) ∀j,${{SOC_{j + 1}} = {{SOC_{j}} - \frac{d_{{RT},j}}{\eta_{d}} + {\eta_{c}c_{{RT},j}{\forall j}}}},$ and0≤SOC _(j+1) ≤E _(ESS) ∀j; applying a balanced combination of mean andvariance maximizes$\max\limits_{q_{X},d_{RT},c_{RT},P_{ESS},E_{ESS}}\{ {\mu_{\overset{\sim}{\vartheta}} - {\lambda( {{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}{\gamma_{m}{()}}^{2}} - \mu_{\overset{\sim}{\vartheta}}^{2}} )}} \}$ where:${\mu_{\overset{\sim}{\vartheta}}\overset{def}{=}{\sum\limits_{\substack{0 \leq m \leq M \\ 0 \leq k \leq K}}\gamma_{m}}},$ and

(q _(RT,m) +d _(RT) −c _(RT) −q _(DA) −q _(X))^(T) ·p _(RT,k)−δ_(RT,m,k)−c _(P) P _(ESS) −c _(E) E _(ESS); and applying a linear programmingframework based on utility functions optimizes through one of maximizingutility, maximizing utility subject to a minimum return constraint,minimizing dispersion while subject to a minimum return constraint, ormaximizing utility relative to a no risk scenario, where:μ_(j)

Σ_(1≤t≤T) w _(t) r _(j,t);{tilde over (ϑ)}_(t) =x ^(T) ·r _(t)−Σ_(1≤j≤3J+3) x _(j) r _(j,t) ;x_(3J+3)=1; ${x\overset{def}{=}\begin{bmatrix}q_{X} \\d_{RT} \\c_{RT} \\P_{ESS} \\E_{ESS} \\x_{{3J} + 3}\end{bmatrix}};$ ${r_{k}\overset{def}{=}\begin{bmatrix}{- p_{{RT},k}} \\p_{{RT},k} \\{- p_{{RT},k}} \\{- c_{P}} \\{- c_{E}} \\{( {q_{{RT},m} - q_{DA}} )^{T} \cdot p_{{RT},k}}\end{bmatrix}};$ maximizing utility uses a linear programming goal${\max\limits_{x}\{ {{\mu(x)} - {\lambda{\rho(x)}}} \}},{{\rho(x)}\overset{def}{=}{Dispersion}},$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {\sum\limits_{t}{d_{t}^{-}w_{t}}} )}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}} \}},$a minimax through:${\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}}} \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}}}}} )}:}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}}}\end{Bmatrix}},$  with η

VaR at optimum and β

probability of returns≤VaR, 0<β≤1, a Gini mean difference:${\max\limits_{x,u}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {{\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\substack{t \\ j}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\substack{1 \leq t^{\prime} \leq {T - 1} \\ {t^{\prime} + 1} \leq t^{''} \leq T}}{u_{t^{\prime},t^{''}}w_{t^{\prime}}w_{t^{''}}}}}} )}:}} \\{{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{''}}x_{j}}}};} \\{{{\forall t^{\prime}} = {{1\ldots T} - 1}};{t^{''} = {t^{\prime} + {1\ldots T}}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{\sum\limits_{h}{{\omega_{h}( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\lambda( {{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta_{h} + {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}} )}:}} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{\omega_{h} \in {weights}}}\end{Bmatrix}};$ maximizing utility subject to a minimum returnconstraint uses a linear programming goal$\max\limits_{x}\{ {{{\mu(x)} - {{\rho(x)}:{\mu(x)}}} \geq \mu_{0}} \}$ and applies one of: a mean absolute deviation through:${\max\limits_{x,d^{-}}\begin{Bmatrix}{{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};} \\{{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ a minimax through:${\max\limits_{x,v}\{ {{{{\sum\limits_{j}{x_{j}\mu_{j}}} - {v:v}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\max\limits_{x,d^{-},\eta}\begin{Bmatrix}{{{\eta - {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}}}} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};} \\{{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ a Gini mean difference:${\max\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{\substack{t \\ j}}{r_{j,t}x_{j}w_{t}^{2}}} + {2{\sum\limits_{\substack{1 \leq t^{\prime} \leq {T - 1} \\ {t^{\prime} + 1} \leq t^{''} \leq T}}{u_{t^{\prime},t^{''}}w_{t^{\prime}}w_{t^{''}}}}}} ):} \\{{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{''}}x_{j}}}};{{\forall t^{\prime}} =}} \\{{{1\ldots T} - 1};{t^{''} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\max\limits_{x,d^{-}}\begin{Bmatrix}{\sum\limits_{h}{{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}:}} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} =}} \\{{1\ldots T};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}};$ minimizing dispersion while subject to a minimum returnconstraint uses a linear programming goal$\min\limits_{x}\{ {{{\rho(x)}:{\mu(x)}} \geq \mu_{0}} \}$ and applies one of: a mean absolute deviation through:${\min\limits_{x,d^{-}}\{ {{{\sum\limits_{t}{d_{t}^{-}w_{t}:d_{t}^{-}}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a minimax through:${\min\limits_{x,v}\{ {{{v:v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} )x_{j}}}};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}} \}},$a conditional value at risk through:${\min\limits_{x,d^{-},\eta}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{d_{t}^{-}w_{t}:}}}} \\{{d_{t}^{-} \geq {\eta - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{t}^{-} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ a Gini mean difference:${\min\limits_{x,u}\begin{Bmatrix}{( {{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{\substack{t \\ j}}{r_{j,t}x_{j}w_{t}^{2}}} - {2{\sum\limits_{\substack{1 \leq t^{\prime} \leq {T - 1} \\ {t^{\prime} + 1} \leq t^{''} \leq T}}{u_{t^{\prime},t^{''}}w_{t^{\prime}}w_{t^{''}}}}}} ):} \\{{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{\prime}}x_{j}}}};{u_{t^{\prime},t^{''}} \leq {\sum\limits_{j}{r_{j,t^{''}}x_{j}}}};{{\forall t^{\prime}} =}} \\{{{1\ldots T} - 1};{t^{''} = {t^{\prime} + {1\ldots T}}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}},$ or a weighted conditional value at risk:${\min\limits_{x,d^{-}}\begin{Bmatrix}{{\sum\limits_{j}{x_{j}\mu_{j}}} - {\sum\limits_{h}{{\omega_{h}( {\eta_{h} - {\frac{1}{\beta_{h}}{\sum\limits_{t}{d_{h,t}^{-}w_{t}}}}} )}:}}} \\{{d_{h,t}^{-} \geq {\eta_{h} - {\sum\limits_{j}{r_{j,t}x_{j}}}}};{d_{h,t}^{-} \geq 0};{{\forall t} =}} \\{{1\ldots T};{\omega_{h} \in {weights}};{{\sum\limits_{j}{x_{j}\mu_{j}}} \geq \mu_{0}}}\end{Bmatrix}};$ and maximizing utility relative to a no risk scenariouses a linear programming goal${\max\limits_{x}\{ {( {{\mu(x)} - r_{0}} )/{\rho(x)}} \}},{r_{0}\overset{def}{=}{{risk}{free}{return}}}$ and applies one of: a mean absolute deviation through:${ {\max\limits_{\overset{\sim}{x},\overset{\sim}{d^{-}},z}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:{\sum\limits_{t}{\overset{\sim}{d_{t}^{-}}w_{t}}}}} = z};{\overset{\sim}{d_{t}^{-}} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};} \\{{\overset{\sim}{d_{t}^{-}} \geq 0};{{\forall t} = {1\ldots T}};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}},$ a minimaxthrough:${ {\max\limits_{\overset{\sim}{x},\overset{\sim}{v}}\begin{Bmatrix}{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:}} \\{{\overset{\sim}{v} = z};{\overset{\sim}{v} \geq {\sum\limits_{j}{( {\mu_{j} - r_{j,t}} ){\overset{\sim}{x}}_{j}}}};{{\forall t} =}} \\{{1\ldots T};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq 0}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}},$ aconditional value at risk through:${ {\max\limits_{\overset{\sim}{x},\overset{\sim}{d^{-}},z,\eta}\begin{Bmatrix}{{{{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}} - {r_{0}z:{\sum\limits_{j}{{\overset{\sim}{x}}_{j}\mu_{j}}}} - \eta + {\frac{1}{\beta}{\sum\limits_{t}{\overset{\sim}{d_{t}^{-}}w_{t}}}}} = z};} \\{{\overset{\sim}{d_{t}^{-}} \geq {\eta - {\sum\limits_{j}{r_{j,t}{\overset{\sim}{x}}_{j}}}}};{\overset{\sim}{d_{t}^{-}} \geq 0};{{\forall t} =}} \\{{1\ldots T};{{\sum\limits_{j}{\overset{\sim}{x}}_{j}} = z};{{\overset{\sim}{x}}_{j} \geq \mu_{0}}}\end{Bmatrix}}\Rightarrow x  = {\overset{\sim}{x}/z}};$ applying,by the one or more computing devices, a linear programming frameworkfactoring risk and return preferences of an owner or operator of therenewable power generator, and computing an optimal DA and RT strategy;controlling, by the one or more computing devices or the one or morecontrol devices in communication with the one or more computing devices,the renewable power generation; placing, by the one or more computingdevices through a market interface to a day ahead energy market, optimalDA commitments for the renewable power generator; and generating anddelivering power to the power grid based on the optimal DA and RTstrategy or according to the optimal RT generation schedule.
 5. A methodto manage operation of a renewable power generation asset, comprising:operating one or more computing devices controlling or in communicationwith one or more control devices of a renewable power generator;obtaining, by the one or more computing devices, historical datapertinent to the renewable power generator as a node within anelectrical supply grid, wherein the electrical supply grid is within aregulated environment; generating, by the one or more computing devicesand based on the obtained historical data, forecasts of power demand,power supply, generation from the renewable power generator, and costsof operating the renewable power generator; identifying, by the one ormore computing devices, optimal DA power commitments of the renewablepower generator across a range of scenarios weighted by probability;estimating, by the one or more computing devices, RT schedules forgeneration by the renewable power generator across a range of scenariosweighted by probability; applying, by the one or more computing devices,a linear programming framework factoring risk and return preferences ofan owner or operator of the renewable power generator, and computing anoptimal RT generation schedule; computing, by the one or more computingdevices, an optimal RT curtailment, energy storage, and energy dispatchstrategy using cost minimization through:$\min\limits_{q_{X},d_{RT},c_{RT}}\{ {\sum\limits_{\substack{0 \leq i \leq I \\ 0 \leq m \leq M}}{w_{i,m}{\gamma_{m}( {( {l_{{RT},i} - q_{{RT},m} - d_{RT} + c_{RT} + q_{X}} )^{T} \cdot c_{{RT},i,m}} )}}} \}$where q_(x) is a real time curtailment of renewable generation, c_(RT)is a RT optimal charge schedule of a storage system, d_(RT) is a RToptimal discharge schedule of the storage system, P_(ESS) is a ratedpower of the storage system, E_(ESS) is a rated energy of the storagesystem, M is a number of possible renewable generation scenarios,indexed by 1≤m≤M, J is a number of discretized time steps in anoptimization horizon, indexed by 1≤j≤J, I is a number of possible powerdemand scenarios, indexed by 1≤i≤I, q_(RT,m) is a real time renewablegeneration forecast in an m^(th) scenario, l_(RT,m) is a demand forecastin an i^(th) scenario, w_(i,m) is defined to be the joint probability of(l_(RT)=l_(RT,i); q_(RT)=q_(RT,m)), γ_(m) is a factor biasingoptimization towards specific generation forecast scenarios,d _(RT,j) ≤P _(ESS) ∀j,c _(RT,j) ≤P _(ESS) ∀j,${{SOC_{j + 1}} = {{SOC_{j}} - \frac{d_{{RT},j}}{\eta_{d}} + {\eta_{c}c_{{RT},j}{\forall j}}}},$0≤SOC _(j+1) ≤E _(ESS) ∀j, η_(d)

Discharge efficiency, and η_(c)

Charge efficiency; controlling, by the one or more computing devices orthe one or more control devices in communication with the one or morecomputing devices, the renewable power generation; and generating anddelivering power to the power grid according to the optimal RTgeneration schedule.